From c7bc0c880f5afa3ec3136ecd2b09b5a352a27e52 Mon Sep 17 00:00:00 2001 From: gallg Date: Mon, 20 Jan 2025 15:03:11 +0100 Subject: [PATCH] warnings notebook added --- .gitignore | 1 + README.md | 4 +- notebooks/example.ipynb | 4 +- notebooks/warnings.ipynb | 226 +++++++++++++++++++++++++++++++++++++++ 4 files changed, 232 insertions(+), 3 deletions(-) create mode 100644 notebooks/warnings.ipynb diff --git a/.gitignore b/.gitignore index 9f4d28d..72036ca 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ .idea/ +.vscode/ manuscript/_build/ manuscript/exports/ **/.DS_Store diff --git a/README.md b/README.md index cc3ecc8..cde4143 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,9 @@ models. stop = res.estimated_stop This prints out the results and plots the learning and power curves. -An extended, working example can be found in ["our example notebook"](notebooks/example.ipynb) + +- An extended, working example can be found in ["our example notebook"](notebooks/example.ipynb) +- A description of possible warnings frequently encountered during runtime can be found in ["our warnings notebook"](notebooks/warnings.ipynb) ## Documentation The package documentation is available [here](https://pni-lab.github.io/adaptivesplit/). diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb index 5335dd1..d589062 100644 --- a/notebooks/example.ipynb +++ b/notebooks/example.ipynb @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "b624c5e52506fe96", "metadata": { "ExecuteTime": { @@ -82,7 +82,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Calculating learning curve: 100%|██████████| 1200/1200 [00:26<00:00, 45.85it/s]\n", + "Calculating learning curve: 100%|██████████| 1200/1200 [00:22<00:00, 53.68it/s]\n", " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", diff --git a/notebooks/warnings.ipynb b/notebooks/warnings.ipynb new file mode 100644 index 0000000..1db01bf --- /dev/null +++ b/notebooks/warnings.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6f4792c6", + "metadata": {}, + "source": [ + "### Pygam Runtime Warning: \"divide by zero encountered in scalar divide\"\n", + "#### reproducibility and explanation" + ] + }, + { + "cell_type": "markdown", + "id": "62629162", + "metadata": {}, + "source": [ + "To reproduce the warning we run the same code as in example.ipynb. The warning is generated by the following line: adaptivesplit/sklearn_interface/split.py:320\n", + "\n", + "```Python\n", + "gam = LinearGAM().gridsearch(gam_X, gam_y, n_splines=np.arange(1, 6))\n", + "```\n", + "\n", + "Here we fit a linear Generalized Additive Model (GAM) with the indices of the learning or power curve (gam_X) and the actual curve (gam_y) to get a smoothed version of the same. Pygam will try to fit different models with different parameters using a Gridsearch algorithm, these parameters are not user defined, but chosen internally by Pygam.\n", + "\n", + "In our code we specify \"n_splines=np.arange(1, 6)\", this means that we want to try 6 combinations of hyperparameters of which none of them are equivalent. This code produces some hyperparameter combinations that return the warning. The problem can be easily solved by simply reducing the number of combinations.\n", + "\n", + "Let's run the example code for the adaptivesplit package with our default number of combinations: n_splines=np.arange(1, 6)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-02T12:29:32.433655624Z", + "start_time": "2023-10-02T12:29:32.391389365Z" + }, + "collapsed": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating learning curve: 100%|██████████| 1200/1200 [00:23<00:00, 51.01it/s]\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "/home/giuseppe/anaconda3/envs/pytorch/lib/python3.11/site-packages/pygam/pygam.py:1150: RuntimeWarning: divide by zero encountered in scalar divide\n", + " r2['explained_deviance'] = 1.0 - full_d.sum() / null_d.sum()\n", + "/home/giuseppe/anaconda3/envs/pytorch/lib/python3.11/site-packages/pygam/pygam.py:1150: RuntimeWarning: divide by zero encountered in scalar divide\n", + " r2['explained_deviance'] = 1.0 - full_d.sum() / null_d.sum()\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "/home/giuseppe/anaconda3/envs/pytorch/lib/python3.11/site-packages/pygam/pygam.py:1150: RuntimeWarning: divide by zero encountered in scalar divide\n", + " r2['explained_deviance'] = 1.0 - full_d.sum() / null_d.sum()\n", + "/home/giuseppe/anaconda3/envs/pytorch/lib/python3.11/site-packages/pygam/pygam.py:1150: RuntimeWarning: divide by zero encountered in scalar divide\n", + " r2['explained_deviance'] = 1.0 - full_d.sum() / null_d.sum()\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + " 0% (0 of 5) | | Elapsed Time: 0:00:00 ETA: --:--:--\n", + "100% (5 of 5) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AdaptiveSplitResults(stop=True, predicted=False, estimated_stop=13823, current_sample_size=13828, score_if_stop=-0.5303170958726394, score_if_stop_now_ci=(-0.67938096732721, -0.5210604463474635), power_if_stop_now=-0.04007880548353543, power_if_stop_now_ci=(-0.39743750570134867, 0.7068074100269603), score_predicted=None, score_predicted_ci=(None, None), power_predicted=None, power_predicted_ci=(None, None))\n" + ] + }, + { + "data": { + "image/png": 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mzZqYNm0aVq9ejR07duCll15Cv3798NZbb2mV60ePHkEqlRbJYuPj4wNXV1c8evRIZ3mNGjWKyFW1atVi/ZXLQ926dXW+Ozk5wdfXV6uE83LUr19fZz1bW1vUqlVLR86XXnoJ8+bNQ3Z2Nk6ePAlfX1+0aNECzZo1w8mTJ9GjRw+cOnUKb7zxhnab8vz3AIrNolMa1apV0+nrinL37l0AwPDhw0tcJzU1tYiLEEGUBinQBCEQx44dQ1xcHHbt2oVdu3YV+X3Hjh3lUqCvX78OGxsb7cPKkParV6+O7t27l/NIysdbb72FDRs24KeffsKHH36In376CQ0bNjRaKrSAgAC8++67GDBgAGrVqoUdO3YUUaA7deqkk1rOEvD19cXgwYMxcOBANGrUCD///DO+//57yGT638aDgoJw+fJlPH78uFjlDgCuXr0KgFlPC1K3bl2Tnxs8Hh4e2n317NkTDRo0wCuvvIJ169Zprb8ajQZeXl4lBsV6enoCAFJSUtC5c2c4OztjwYIFqF27Nuzs7PDvv/9i5syZOpbHVatWYcSIEdi7dy8OHz6MyZMnY8mSJTh79iyqV6+uXU/fVIc2NjbFLucM9NU3BR07doRSqURERAROnjyptZi/9NJLOHnyJG7duoXExEQdS7q+/z1PwREAfSjv+mWN1vF9vGLFihLvM/wIHkHoCynQBCEQO3bsgJeXF7788ssiv+3Zswf/+9//sHHjRr0eJo8fP8Y///yDkJAQreXQmO0bk7Zt26J27drYuXMnevTogRs3bhSbPaSiVK1aFbVr18b169eN3raQyOVyNG3aFHfv3sWzZ8/g4+Oj97avvPIKfvrpJ2zbtg2ffvppkd/T0tKwd+9eNGjQoMRc4WVhCuXw5ZdfRufOnbF48WKMGzcOjo6OqF27No4cOYIOHTqUeg4fP34cz58/x549e3SC4ErKHtKkSRM0adIEn376Kc6cOYMOHTpg48aN+PzzzxEQEACNRoO7d+9q82oDwNOnT5GSkqIN/DQ1d+/e1Qmsy8jIQFxcHPr06QMgPwD19u3bqFWrlna9vLw8PHjwQOdFqE2bNrC1tcXJkydx8uRJrStUp06d8M033+Do0aPa7zz6/vfGpmrVqkVcl/Ly8hAXF1fqdvwIk7Ozs9leAgnrh3ygCUIAsrOzsWfPHrzyyisYNGhQkWnixIlIT0/Hvn37ymwrKSkJQ4YMgVqtxieffGL09k3BsGHDcOnSJcydOxcSiQRDhw41uK0rV64U65bw6NEj3Lx5s8gwttDom8bu7t27ePz4cZHlKSkpiIiIQNWqVYtY+spi0KBBaNiwIZYuXVqkyqJGo8H48eORnJyMuXPnlqtdHr7QS1luIoYwc+ZMPH/+HN988w0A4I033oBarcbChQuLrKtSqbSKFm8FLqjY5+Xl4auvvtLZJi0trUi2kyZNmkAqlWr96HkFde3atTrrrV69GgBT9A1B3zR2PJs3b9bJdPH1119DpVKhd+/eAIDu3bvD1tYWX3zxhc5xf/vtt0hNTdWR087ODq1bt8ZPP/2Ex48f61igs7Oz8cUXX6B27drw9fXVbqPvf29sateurfXX5tm8eXOZFuiWLVuidu3aWLlypU6xI57ExESjyklUDsgCTRACsG/fPqSnp6Nfv37F/t6uXTt4enpix44dePPNN7XL79y5g+3bt4PjOKSlpeHKlSv45ZdfkJGRgdWrV6NXr14Vat9cvPXWW1iwYAH27t2LDh06IDAwsMR1//rrL22gV0Hat2+PWrVqITw8HHPnzkW/fv3Qrl07ODk5ITo6Glu3bkVubm6Fykpv3boVBw8eLLL8gw8+MLjNDRs2YP78+fj7779LDSS8cuUKhg4dit69e+Oll16Cm5sbYmJi8MMPPyA2NhZr164t0UWgJGxtbfHrr78iNDQUHTt21KlEuHPnTvz777+YPn16sSnZ9CEmJgZBQUGYO3duhf734ujduzcaN26M1atXY8KECejcuTPGjRuHJUuW4PLlywgLC4NcLsfdu3fxyy+/YN26dRg0aBDat2+PqlWrYvjw4Zg8eTIkEgl+/PHHIpbyY8eOYeLEiXj99ddRr149qFQq/Pjjj7CxscHAgQMBAM2aNcPw4cOxefNmrWvI+fPn8cMPP+DVV181ON0an8JO30DCvLw8hIaG4o033sDt27fx1VdfoWPHjtrr3dPTE7Nnz8b8+fPRq1cv9OvXT7te69ati1T7fOmll7B06VK4uLigSZMmAFj10Pr16+P27dtFcizr+98bm9GjR+O9997DwIED0aNHD1y5cgWHDh0q0x1LKpViy5Yt6N27Nxo1aoSRI0eiWrVqiImJwd9//w1nZ2dtikeC0BvhEoAQROWlb9++nJ2dXakVtkaMGMHJ5XJtxTkUSOMmlUo5V1dXrnnz5twHH3zA3bhxwyjtl5YmrzCGpLErSOvWrTkA3FdffVXs76WlsQPAfffddxzHsUqLn332GdeuXTvOy8uLk8lknKenJ/fyyy/rpBXjuJIrEZZ330+ePDF5GrunT59yS5cu5Tp37sz5+vpyMpmMq1q1KtetWzfu119/LXG70tLY8SQkJHDTpk3j6tSpwykUCs7V1ZXr3r27NnVdQco6zuLWNXYlQp7vv/9ep+85juM2b97MtWzZkrO3t+eqVKnCNWnShJsxYwYXGxurXef06dNcu3btOHt7e87Pz4+bMWMGd+jQIZ1+iI6O5t59912udu3anJ2dHefm5sZ17dqVO3LkiI4MSqWSmz9/PlezZk1OLpdz/v7+3OzZs7Xp8Mo6js6dO3OdO3cusm550tj9888/3NixY7mqVatyTk5O3LBhw7jnz58XWX/Dhg1cgwYNOLlcznl7e3Pjx4/XpqIrCJ9esnfv3jrLR48ezQHgvv3222Ll0ee/L60/i6Nz585co0aNiv1NrVZzM2fO5Dw8PDgHBweuZ8+e3L1798pMY8dz6dIl7rXXXuPc3d05hULBBQQEcG+88QZ39OhRveUjCB4Jx4komoEgCIIgiGL5/vvvMXLkSFy4cKHMzDQEQZgW8oEmCIIgCIIgiHJACjRBEARBEARBlANSoAmCIAiCIAiiHJAPNEEQBEEQBEGUA7JAEwRBEARBEEQ5IAWaIAiCIAiCIMoBFVIxExqNBrGxsahSpQokEonQ4hAEQRAEQRCF4DgO6enp8PPzg1Rasp2ZFGgzERsbC39/f6HFIAiCIAiCIMrgyZMnqF69eom/kwJtJqpUqQKAdYizs7PA0lgWSqUShw8f1paLJcQB9Yt4sZq+ycwE/PzYfGws4OgorDwVxGr6xQqhvhEfWVlZaNOmDbKysnDp0iW4uLiYZb9paWnw9/fX6m0lQQq0meDdNpydnUmBLidKpRIODg5wdnamG5uIoH4RL1bTNzY2+fPOzlahQFtFv1gh1Dfiw9nZGffv38eBAwfg4+Nj9n4py92WgggJgiAIgiAIohyQAk0QBEEQBEEQ5YBcOAiCIAiCIAhRkZOTgzfeeANPnz5Ft27dROdaQwo0QRAEQRAEISrUajX++OMP7bzYIAWaIAiCIAiCEBW2trb4+uuvce3aNdja2gotThHIB5ogCIIgCIIQFXK5HKNGjRJtakFSoAmCIAiCIAiiHJACTRAEQRAEQYgKjUaDGzdu4PHjx9BoNEKLUwTygSYIgiAIgiBERXZ2Npo3bw4AePvtt6FQKASWSBdSoAmCIAiCIAjR4eHhgby8PKHFKBZSoK2U9HTgwQOA44r/vXCFyrK+G7KNsdrgs9dERwOyYs5YQ/ZrqHzGbstQSupXU1J4n3y/xMWxisv6/i9CLS9vG4Ux5D8XahuVin0mJuZfM2Udpyh/zwRcX8ympABQsvnCx1vcf1bWOkJ856+Z2Nj8KuVl9bc+54M52tB3HVNsa479FHzOFKwgX9p5K/bfSnsGlTSv73rmadsRt2/H4uTJA7C3d4TYIAXaSsnMBO7eBezsSl/PXDdWY9w8o6J0v0skuu0W/q7vMv5iLW9bJW1XVlvFtW0I5VXITbXPS5fK/g8MXW6MNsqDsfqmNPRtv6Rj0nd9iQS4cKH0NsraR0Vf+gxVBvjfbXKA3i++R0QAarvyK66FKc+1aYrvALtmClLwv9CnL4zRn4a2oe86+lLceWCKa1Dfc/nWrZJlKO24S7t3CP1bwe/6bqfvvbDwNqWtV9x8Wb/xbSckAP7+ZctjTkiBtmJsbAAfH6GlqDgaDbPYVKsGSCnsVTTw/eLnZxn9oo+VsiwMUciFwBR9U9GX5NJ+L+k3aXb+vIcHoLHP/26METFzQ/cy8WJp9zMxU/B6Lmm+uO/FLVergWfPWP+IDYs5TZKSkjBs2DA4OzvD1dUVo0aNQkZGRqnbdOnSBRKJRGd67733tL9fuXIFQ4YMgb+/P+zt7REUFIR169bptHH8+PEibUgkEsTHx5vkOAmCMA0SSdFJKi3fVFwbpU3WRFnHWtZ/Z2NT8iSTlTzxFF5euI2y+oogCPNQ0n1B3+teLmeTRpODhQvfwerVq5GTkyP0YRXBYizQw4YNQ1xcHMLDw6FUKjFy5EiMHTsWO3fuLHW7MWPGYMGCBdrvDg4O2vnIyEh4eXlh+/bt8Pf3x5kzZzB27FjY2Nhg4sSJOu3cvn0bzs7O2u9eXl5GOjKCIAiCIAiiIBqNGocO7dLOiw2LUKCjoqJw8OBBXLhwAa1atQIArF+/Hn369MHKlSvh5+dX4rYODg7wKcGP4d1339X5XqtWLURERGDPnj1FFGgvLy+4urpW7EAIgiAIgiCIMpHLbTF16kqkpt6EXC6+Ut4WoUBHRETA1dVVqzwDQPfu3SGVSnHu3DkMGDCgxG137NiB7du3w8fHB3379sWcOXN0rNCFSU1NhZubW5HlwcHByM3NRePGjTFv3jx06NChVJlzc3ORm5ur/Z6WlgYAUCqVUCqVpW5rDPgofDH6DZUXjUap80mIA+oX8WI1fVNAfo1GafHHYzX9YoVQ34gPqRR4883xiI8Ph1QKs+hOgP77sQgFOj4+vojLhEwmg5ubW6m+yEOHDkVAQAD8/Pxw9epVzJw5E7dv38aePXuKXf/MmTPYvXs3/vzzT+0yX19fbNy4Ea1atUJubi62bNmCLl264Ny5c2jRokWJ+16yZAnmz59fZPnhw4dLVeCNiUTCgiKshfj4cKFFIIqB+kW8WHrf2BTwe4yLOwR1WWmFLARL7xdrhvpGnFy7Fo5r18yzr6ysLL3WE1SBnjVrFpYtW1bqOlGFc5eVg7Fjx2rnmzRpAl9fX4SGhuL+/fuoXbu2zrrXr19H//79MXfuXISFhWmX169fH/Xr19d+b9++Pe7fv481a9bgxx9/LHHfs2fPxrRp07Tf09LS4O/vj7CwMB1falPx9Clw8SKL9rZ0NBol4uPD4ePTA1KpXGhxiBdQv4gXa+kbaXamdt7Xtyc0IswFWx6spV+sEeob8aHRaBAbG43ExJPo1m0oAgLMU4mQ9xgoC0EV6OnTp2PEiBGlrlOrVi34+PggISFBZ7lKpUJSUlKJ/s3F0bZtWwDAvXv3dBTomzdvIjQ0FGPHjsWnn35aZjtt2rTBqVOnSl1HoVAUW3ZSLpdDLjf9xclHr1tTOh6pVE43NhFC/SJeLL1vCsoulcoBCz6Wglh6vwDAvXvApEmArS0QEADUqMGmgAA2eXlZ5vPHGvrGWsjNzcRrrzUEANy8+Qbkciez7FdfHU1QBdrT0xOenp5lrhcSEoKUlBRERkaiZcuWAIBjx45Bo9FolWJ9uHz5MgDmlsFz48YNdOvWDcOHD8eiRYv0bqdgGwRBEARRmdi7l1W6BICYGODMGd3fFQpW+IJXrnnFukYNwMXF/PISlomdnQM4TnwZOAAL8YEOCgpCr169MGbMGGzcuBFKpRITJ07E4MGDtRk4YmJiEBoaim3btqFNmza4f/8+du7ciT59+sDd3R1Xr17F1KlT0alTJzRt2hQAc9vo1q0bevbsiWnTpmn9qW1sbLSK/dq1a1GzZk00atQIOTk52LJlC44dO4bDhw8L82cQBEEQhMCcPcs+x48H3N2BR4+Ax4/Z53//Abm5zEp9717RbV1cdBVq/tPfnyneBAEA9vaOOHEiBbGxB+DgID73LYtQoAGWTWPixIkIDQ2FVCrFwIED8cUXX2h/VyqVuH37ttb529bWFkeOHMHatWuRmZkJf39/DBw4UMdF49dff0ViYiK2b9+O7du3a5cHBATg4cOHAIC8vDxMnz4dMTExcHBwQNOmTXHkyBF07drVPAdOEARBECIiPh548IC5aLz+OlA4rEelYus8fMiUal6xfvyYxeekpgJXr7KpIBIJ4Oub7w5SowYQGMg+vb1Z8Q2CEAsWo0C7ubmVWjQlMDAQXIH6j/7+/vjnn39KbXPevHmYN29eqevMmDEDM2bMKJesBEEQBGGt8NbnRo2KKs8Ai8GpXp1NhcnO1lWqC1quMzJY5qjY2Px98NjaMgt1YXeQgACASjRYNyWV/BYai1GgCYIgCP149gxIS5OjlBpTBGEwvHLbrl35t7W3B+rXZ1NBOA5ITi6qWD9+DDx5AuTlAffvs6kwLi66QYz8p78/YCWZDysleXm5+PDD93H9uhJ//hmKgABxBXeSAk0QBGFFJCcDb74pg6NjJ+zZwyx3BGEs1Grg/Hk2b4gCXRISCeDmxqbg4KL7jIvTdQXhlWzeJeTaNRSbJ9jHp6jVukYN5ipCLiHi5vZtFc6c2QoA2LhxAzp2FFigQpACTRAEYUWcPQukp0uQnu6EU6dU6NZNaIkIayIqCkhLA5ycmAuHObCxyXcJad9e97ecHGahLuwO8ugRkJ7OfLHj4/OVfh65XNclxN9fglq1FDRqIxLi44GPPpID+BxeXpmYOVN8bzukQBMEYRC7d0uxeXNvLF0qQTmySRImJiIif/6PP6SkQBNGhXffaN06v96AkNjZAXXrsqkgHMcs08Up1v/9x1xCoqPZxJChSpVu2Ly5aFuEeUlNZTnGnz2zRa1aH2PBgr/g7Cy+oTQRnP4EQVgap04Bq1dLwXE2+P13DSnQIoHjgHPn8r+fOSPBs2eAh4dwMhHWBf+CFhIirBxlIZGw4EJXV6BZM93f1Grm+lFQuT53jsOjR7aYPJnDt9+CLNECkZsLTJ/Osrx4eQHr1qmgViuFFqtYLLBOEEEQQvLwIfDJJwDHSQAA585JoBZnnvtKx717wPPngJ0dh7p1k6FWS7B/v9BSEdZCRgZw/Tqbt+SXZhsbpiCHhACDBwMzZgDffKNCjRppSEyUYMIEFohLmBe1GpgzB7h8mbkIrVvHwdY2EampqTpZ1sQCKdAEQehNRgazDmRmAs2aaeDgoERamgRRUUJLRgD51sGWLTn07PkQALBvn3jTQBGWxcWLTMmpUQOoVk1oaYyLqyswb14EfH05PHkCTJ7MfKgJ88BxwKpVwLFjzD991SqgevUs9OxZDcOHD0d2dpbQIhaBFGiCIPRCo2HWgUePWFGDpUvVaNqU1fIt6HdLCAfvn9q2LYcOHWJgb8/h8WPgyhVh5SKsA/46N2b2DTHh5paDDRtUcHcH7twBpkxhQYqE6dm2Dfj5Z+Z6s2AB0LKl0BKVDSnQBEHoxcaNwMmTrNTuihWsfG/z5gkASIEWAzk5bOgTANq21cDeXo3u3Znpee9e4eQirIeK5H+2FPz9gQ0bgCpV2IvnjBmAUpwuuFbDgQPA+vVsfto0oEcPNm9v74jz5/Pw+++/i7KUNynQBEGUyZEjwFaWjhOffAI0bMjmeQX6xg0a7hSaf/9lmQW8vVn5YwDo108DgPVfZqZwshGWz5MnQEwMy7zRqpXQ0piWunWBNWuYseDMGWDuXFCch4k4exaYP5/Nv/02MGSIsPKUB1KgCYIolbt3Ab7i/bBhQJ8++b95eWUjIIDTKa5ACENB66CExXeiaVMOAQGsfPKRI8LJRlg+/PnVtCng4CCsLOYgOJiNtMlkwOHDbJ5iCYzLrVvMwq9WAz17stR1lgQp0ARBlEhKCvDhh8w9oG3b4m9w7doxKye5cQhLccPrEgnQrx+bJzcOoiJYSvo6Y9K+PfPHlUiAX38Fvv5aaImsh9hY4IMPgKwsllN87lxAWkgjzcvLxerV07Flyxbk5uYKI2gpkAJNEESxqFTA7Nls2LZaNWDx4uILJ7Rrx8wyERFkoRGKp09ZQQiJhD2MCvLyyyxt19WrLAUhQZQXlYpl4ACs2/+5OMLCgJkz2fzWrcD27cLKYw2kpDBjzPPnzF1mxQrAtpg6KWq1Crt2rcf+/fuhVqvMLmdZkAJNEESxrFsHXLgA2NuzlEIuLsWv16IFB1tbpsSRgiYMfPGUhg1ZOq6CeHjklz/et8+sYhFWwrVrzFLo6grUry+0NOZn0CBgwgQ2v3YtXUcVISeHBQo+egT4+ABffMFyPheHTCbHiBEzMWjQIMhkcvMKqgekQBMEUYT9+4GffmLz8+cDdeqUvK69PfMXBMiNQyjKyo7Qvz/7/PNPZk0kiPLAX9dt2xYdZq8sjBgBvPUWm//8c+DvvwUVxyJRq1kQ+tWrgLMzy7zh6Vny+nK5Ld5/fyHeeust2BZnohaYSnopEARREtevM3cNABg9GujWrexteL9IXpEjzIdGk2+BLkmB7tgRcHNjQ6anT5tPNsI6qAzp68pCImE+u/36sWvu448pcLo8cBxz1fjnH+ausXo1ULOm0FJVDFKgCYLQ8uwZi4rOywM6dQLGjtVvO16BjoykwgPm5tYtIDUVcHQEmjQpfh2ZjPlCAzT8TJSPlBRoK41WZgUaYEr0xx8DXbuy3NAffshSeBJl8913LBBTImEWfH7UsjQ4jkN2diZycnKolDdBEOIlL48pzwkJzDKwYIH+w7W1a7OhuNzc/GIehHngrYOtWhUf5MnDZ+M4dYq9KBGEPpw/z6yH/DVe2ZHJmALYpg3zC588mQXwEiXzxx/AV1+x+Q8/1G9UEwBycrLQuXNVDB48mEp5EwQhTvjhtatXWUDHypUlB3YUh0SSb50iP2jzou/wes2aLIevWs0qfxGEPlTG9HVlwVdjbdSIjf5MnMjSshFFOXOGvXAAzI/8zTcFFceokAJNEAR++w343/+YIrx4MRAQUP42+AcsKdDmIzOTlRsG9FNweCv0vn2UcpAoG44r27++suLoyDIV1arFRu0mTGAxBkQ+UVEsBaBazQpw8ZlM9MXOzgH//JOMXbt2wd5efNV7SIEmiErOv/8yawrAbnB8yrPy0qYNU8Cjo1lKO8L0REayh1O1akD16mWv36MHYGfH0g1eu2Zy8QgL58EDphwqFPr5rFY2XF2BDRsAX19W6nzSJCA9XWipxMF//7Ggy+xslr1lzpz8Cqn6IpFIYG/vCDs7O0jKu7EZIAWaICox8fH5FoKwMGD4cMPbcnVleYgBysZhLsqbHcHRkSnRAFUmJMqGH01q3py9eBFF8fJi/r3u7sCdO8DUqRRInZzMfMOTklje8OXLAbn40jhXGFKgCaKSkpPDAjqSk4F69YDPPiu/haAwlM7OvPD/c3n8U3k3jvBwFgRFECVB7hv64e/Pcho7ObEg6pkzK2++9exsYMoU4PFjwM+Pubk4OhrWllKZh6++moPt27cjLy/PqHIaA1KgCaISwnEssOPWLWY5XrXKOBYm/kF77hyzahOmIyaGPaRsbFgGDn0JDgZq1GDK89GjJhOPsHByc5mLEEAKtD7Uq8eqFCoULNf63LksX3RlQqViaf5u3GCVa7/4glVCNbw9Jb7/fhl+/fVXqFRK4wlqJEiBJohKyPbtwMGDTPlaupT58BmDxo2ZFSYtDbh50zhtEsXDWwf5/1xfJJJ8KzS5cRAlcfkyU6I9PVkKO6JsgoOZu4KNDXDoEIstqSzBuhwHLFsGnDzJXiLWrAECAyvWpo2NDIMHT8Irr7wCG5tScnQKBCnQBFHJiIhgw40AMG1a+ayXZSGTsWBCfj+E6ahIerGXX2Y5vi9fBh49MqpYhJVQ0L9ehPFboqVDB2D+fPaf/fILsGmT0BKZhy1bWCYnqRRYtIilzKwotrYKTJu2CqNHj4ZCoah4g0aGFGiCqEQ8ecKG2DQaZoV84w3j74P8oE2PSgVcuMDmDRle9/TMz7byxx/Gk4uwHvjrt21bYeWwRHr1Yn7QAFMsd+4UVh5T8/vv+S8KM2cCXboIKY35IAWaICoJmZnA9OkszVKTJsCsWaaxLPEK9PXrzJWDMD43bwIZGYCzMxAUZFgb/fuzz/37K2/AE1E8z54Bd+/qFkgiysegQcD48Wx+9Wp2nVkjp04BS5aw+VGjgIEDhZXHnFiMAp2UlIRhw4bB2dkZrq6uGDVqFDIyMkrdpkuXLpBIJDrTe++9p7NO4d8lEgl27dqls87x48fRokULKBQK1KlTB99//72xD48gTIpGw4JaoqNZUMfy5YCtrWn25ePDfN80GlYGmDA+vPtGmzbM39IQOnYEqlZlyhK52xAF4a3PDRqwIGPCMN59Fxg2jM0vXAgcPy6oOEbn+vX8NKh9+wKF1KsKk52diTZtbPHqq68iKyvTuI0bAYtRoIcNG4YbN24gPDwc+/fvx4kTJzB27NgytxszZgzi4uK00/Lly4us89133+ms8+qrr2p/e/DgAV5++WV07doVly9fxpQpUzB69GgcOnTImIdHECZlyxZ285bLWZluT0/T7o+qEpoWY6QXk8tZdTCAVSYkCB5KX2ccJBKW0q1vX6Zkzp4NXLwotFTG4fFjdmy5ucwd7JNPKp+vvPjCGoshKioKBw8exIULF9DqRcTT+vXr0adPH6xcuRJ+fn4lbuvg4AAfH59S23d1dS1xnY0bN6JmzZpYtWoVACAoKAinTp3CmjVr0LNnTwOPiCDMx/HjwObNbH72bJa1wdSEhAA//cQsWRxX+W6spiQ9nVl+gIorOP36ATt2ACdOsDLE7u4Vl4+wbDSa8hfoIUpGImHKZXo6uxdPmwZs3JhfdMoSef6cVV1MSWEuZEuXsgByY2Nn54BDh2IQH39ElKW8LUKBjoiIgKurq1Z5BoDu3btDKpXi3LlzGDBgQInb7tixA9u3b4ePjw/69u2LOXPmwMFBtyMmTJiA0aNHo1atWnjvvfcwcuRIbdnIiIgIdO/eXWf9nj17YsqUKaXKnJubi9zcXO33tBfOoEqlEkql6fMZ8j6N1pCHUqNR6nwS+nP/PvDZZzIAErzxhhqvvKIx2jlRWr8EBwO2tjI8fSrB/ftK1KplnH0SwPnzEmg0MgQGcvDyUhXbn/peMzVrAo0b2+D6dSkOHFBj2DCR3TAKyK/RKC3+HmAJ97Lbt4HkZDkcHDg0blz8+WWNmLJvpFLmwjF1qg0uXpRi0iQOmzerULOm0XdlcrKygA8+sEFMjBTVqnFYvVoFOzvT6RouLq7IznaBRqOCUmkeS4y+OppFKNDx8fHw8vLSWSaTyeDm5ob4+PgStxs6dCgCAgLg5+eHq1evYubMmbh9+zb27NmjXWfBggXo1q0bHBwccPjwYbz//vvIyMjA5MmTtfv29vbWadfb2xtpaWnIzs6Gvb19sftesmQJ5s+fX2T54cOHiyjwpkIiAWJjzbIrsxAfHy60CBZFRoYcH33UCVlZcjRpkog33ohAbKzxk5KW1C9BQSG4csULhw/fQr9+0Ubfb2Xl6NFmAALRuHE0YmOvl7quPtfMSy8F4Pr1YOzZk4kuXf4W1WiBTYGayHFxh6C2knrSYr6XHT5cF0BDNGoUj8TEyhfEYMq+mT5dhs8+a4+7d6vi/fdVWLLkJLy8sk22P2OjUkmweHFb3LrlDWfnXHzyyUnk5maaRc+4di0c166Zfj8AkKVniVZBFehZs2Zh2bJlpa4TFRVlcPsFfaSbNGkCX19fhIaG4v79+6j9IjP8nDlztOs0b94cmZmZWLFihVaBNpTZs2dj2rRp2u9paWnw9/dHWFgYnJ2dK9S2Pjx9ynytqlUz+a5MjkajRHx8OHx8ekAqlQstjkWgVgNLltggLk4KX18Oq1a5wtW1t1H3UVa/dOkixZUrwM2bjfDeew2Muu/KCscB166x23a3bgHw86tR7HrluWZefx3YupXDkyfOSEp6GU2aiKfygzQ7P3DI17cnNPYG1gQWCZZwL7t5k0Wldu7sBT+/PgJLYz7M1TdffgmMG8fhwQN7LFzYA998o4Kbm8l2ZzQ4Dli40Ab//iuFnR2Hdets0KhRZ5PuU6nMw7ZtK5CRcQ+ffroBtWub5/pP0zN9lKAK9PTp0zFixIhS16lVqxZ8fHyQkJCgs1ylUiEpKalM/+aCtH2R0PLevXtaBbq4dRYuXIjc3FwoFAr4+Pjg6dOnOus8ffoUzs7OJVqfAUChUBSb+Fsul0MuN/2Nk/dHklpMmGjZSKVy0T50xMb69SwQSKEAVq6UwM3NdP9bSf0SEgKsWwdcuiRFXp7UKKXCKzuPHwNxcez6bt1aVub1rc814+wMdO8O/PknsH+/DM2aGVHgClJQdqlUDljJ9S/We1l2NnDlCptv394GUqmBKV4sGFP3jZsbsGEDMHo08OSJBJMny7F5c/mqiQrB11+zVHyseq0ETZqYXn3UaPKwaRMbyf/kkw1m0Z0A6L0fQdUrT09PNGjQoNTJ1tYWISEhSElJQWRkpHbbY8eOQaPRaJVifbh8+TIAwLeUusWXL19G1apVtcpvSEgIjh49qrNOeHg4Qgwp/0UQZuDgQeDHH9n83LlA/frCyFG7NuDlxaK0L10SRgZrg89qEhwMlPL+Xm740t6HDzMliqicREay+Bk/P8DfX2hprBdvb2aJdnMD7twBpk4FCngriY5ffwW+/ZbNz57NUmCaAxsbGfr3fxc9evSgUt6GEhQUhF69emHMmDE4f/48Tp8+jYkTJ2Lw4MHaDBwxMTFo0KABzr9IPHv//n0sXLgQkZGRePjwIfbt24d33nkHnTp1QtMXNSb/+OMPbNmyBdevX8e9e/fw9ddfY/HixZg0aZJ23++99x6io6MxY8YM3Lp1C1999RV+/vlnTJ061fx/BEGUwa1bLFgFAEaMAMLChJOlYBEGSmdnHEyVHaFFC6YwZWYChewFRCWCynebjxo12EihoyMzMMyaJc6CRsePs7oBADB2LFAgy6/JsbVV4JNPNmLChAlUyrsi7NixAw0aNEBoaCj69OmDjh07YjOfmwssavL27dta529bW1scOXIEYWFhaNCgAaZPn46BAwfijwJ1a+VyOb788kuEhIQgODgYmzZtwurVqzF37lztOjVr1sSff/6J8PBwNGvWDKtWrcKWLVsohR0hOpKSWKVBPi8nXwVLSKist/FQKpmFEDC+Ai2RsFy1AOWErszwL7qUvs481K8PrF3LXO1OnQLmzxdX5qyrV1kKPo2GKc5jxggtkbgot01cpVJh8eLFePfdd1G9enVTyFQsbm5u2FlKQfnAwEBwXH7wi7+/P/75559S2+zVqxd69epV5r67dOmCSzQGTYgYlYpZMJ4+ZZaNRYsMr1BnTNq0YX740dFAfDyrUkgYxtWrLIVU1apAvXrGb/+VV1h+2n//Zb7WNYqPTySslLg44NEjdt9o3VpoaSoPzZsDy5Yx48dffwFVqgAffST8CMDDh8y1JDeXuWzMmiW8TGKj3BZomUyGFStWQCXGsQaCqKSsWsUUH0dHNl+litASMVxc8gsGkBW6YvD/X9u2pgkO9vLKHzEoMFBHVBL486tRI/HcPyoLHTsy67NEAvz8c37hK6F49gyYPBlITWXnw5IlpimUUhbZ2Zno1MkVb775pvWU8u7WrVuZ1l2CIMzD778Dv/zCbr4LF0J0yfmprLdxMEd5ZT6YcP9+cfpjEqaDqg8KS69ewIwZbP6bb1glVyHIyGDKc2wsi4tYu9a4AcvlJScnS6conZgw6J2id+/emDVrFq5du4aWLVvC0VE3N18//i5MEIRJuXKFlVEFgPfeAzp1Elae4mjXjj0Qzp9nSpkQlgxLJyUF4FPim1LB6dQJcHUFEhOZQmWuaHtCWNRqdn0C+S+8hPl5/XVm9d24MX8k8ZVXzLd/pZIp8XfusAwh69czlzGhUCjs8fvvd/D06d+wsxNQiy8Bgx5l77//PgBg9erVRX6TSCRQq9UVk4ogiDJJSGA3O5UKCA0F3n1XaImKhx8STk8Hbt4EXiTBIcrBuXOskEGdOoCHh+n2I5cDvXsz69e+faRAVxZu3mTXZ5UqQFCQ0NJUbkaNAtLSgJ072YhilSpAZ9PWKwHA7i8LFrAXKXt7Znk2Y5hbsUilUvj5BQLwhlSERS0Mkkij0ZQ4kfJMEKYnNxf48EPg+XOmVM2dK94AD5mMBRMC5MZhKOYcXu/fn32eOAEkJ5t+f4Tw8OdXmzY0QiQ0EgkwZQqzPKvVLO/yxYum3++XX7IgRhsbFtTIx64QJSM+lZ4giFLhOBbUcfMmC9JbtQpwcBBaqtKhdHaGw3HmVaDr1GEPT5UKOHDA9PsjhIfS14kLqRT49FNmec7LYxk6eBcuU7B7N/D992z+009ZGlQxoFIp8dNPX2Dfvn1QKpVCi1MEgxXof/75B3379kWdOnVQp04d9OvXDydPnjSmbARBFMOuXfklVZcsAapVE1qisuEfzDduMB8/Qn+io5lPskLBKhCaAz6MZe9epsAT1kt6OrsuAVKgxYRMBixeDLRqxQocTZrEUssZm2PHgJUr2fz48fn54MWAUpmHNWs+xNatW6FU5gktThEMUqC3b9+O7t27w8HBAZMnT8bkyZNhb2+P0NDQUnM1EwRRMc6fZ75pAPDBB/muEWLHx4dlB9Fo8oOVCP3grc/NmwN2dubZZ69eTGGPjs5Xrgjr5MIF5ioQEAD4+gotDVEQhYKNMDZsyAKJJ0xg+fSNxeXLzOLMccDAgeKLo5FKbdCz52B06tQJUqkIChsUwiAFetGiRVi+fDl2796tVaB3796NpUuXYiFfR5ggCKMSE8P84dRq5h83ZIjQEpUPKuttGOZIX1cYJyegWzc2T5UJrRtKXyduHB2BL74AAgNZoaz332dVZytKdDQwbRpzEencmQWkiy2ORqGww8KF2zBt2jTYmct6UA4MUqCjo6PRtxg7f79+/fDgwYMKC0UQhC7Z2SxoMDWVWSNmzxbfza4seL+6s2fJLUBfcnNNV767LPhgwkOHgJwc8+6bMA8F/espfZ14cXVlQX4+PqxK6KRJLF+zoSQksDbS0lhWJLFUrrU0DFKg/f39cfTo0SLLjxw5An9//woLRRBEPhzHqlTdvQu4uwMrVrChPUujeXMmd0ICs34QZXP5MlOiPT2B2rXNu+8WLZh/fWYmUMztnrACnjxhBTNkMtbfhHjx9mZKdNWqwO3bzHpsyIttRgZz/3v6lLntrF5tPtcwa8MgBXr69OmYPHkyxo8fjx9//BE//vgj3nvvPUyZMgUffvihsWUkiErNd98BR46wh9zy5exGaonY2TElGiA3Dn0pOLxu7hEHqTQ/oIjcOKwT/vwKDhZ/Jh+CKbzr1zO3jn//ZSOR5akYmpfHRjJ5Y8z69cy6LVayszMRFuaHd955x3pKeY8fPx67du3CtWvXMGXKFEyZMgXXr1/H7t27MW7cOGPLSJSTuDh2YZ08adyAA8L8nDoFfP01m585E2jWTFh5KgrvhkDp7PSD/5/athVm/6+8whT3yEjgv/+EkYEwHZS+zvJo0ABYs4aN5p08yUYnNZqyt9No2LoXL7KXpXXrAD8/08tbUVJSniEtLU1oMYql3CnTVSoVFi9ejHfffRenTp0yhUxEBTl5Mj+n4+rVzG8qOJgpX8HBQK1a5O9kCTx8CHzyCXPhGDQIGDBAaIkqTkgIyyLy779s+JGGDkvm2TNmKZJIhFOgfXxYn505w6zQL4rQElaAUimcfz1RMVq0YMVOpk9nxU+cnZllubRRqvXrWTyDjQ0byWzQwHzyGopCYY+ffrqExMSToizlXW4LtEwmw/Lly6Eqz7gBYVbq1gVGj2Y+k1Ips0IfPMguuCFDWNnnyZOBb79lN1AKEBIfGRns5piZydwepk8XWiLjUKsWc0HJy2NKNFEyfPaNBg2Y36NQ8Dmh9+9nGWAI6+DqVSAri51b9eoJLQ1RXjp2BObNY/O7dwPffFPyujt3Aj/+yOY/+8xyXpikUilq126EGjVqiLKUt0FFO0NDQ/HPP/8gMDDQyOIQxqB5c2DhQjb86+YGXL8OXLnCApKuXWPK2ZkzbAKYb21QUL6VulkzYR/YlR21muXmfPSIKZvLlgFyudBSGQeJhN289+5lw8diqXglRsSSXqxTJ1bxMiGBKfXUZ9ZBQfcgEeomhB707s0yaaxYAWzezCzRgwfrrhMezlw+AGDiRODll80vp7VikALdu3dvzJo1C9euXUPLli3h6Oio83s/3mRBCI6jI7tB8kPAKhUbFuYV6suX2VDxtWts4t9SAwKYQs0r1f7+lpc2zVLZuJH5PisUrEKUm5vQEhmXkBCmQJMfdMloNPkWaKHcN3hsbdmDetcu5sZBCrR1IER+ccL4vPkmqya5cSN7Xjg7A336sN8iI5nFmeOAN94Ahg8XVtbyolIpsW/ft0hJuYZGjXoAEJclySAF+v0XjnCrV68u8ptEIoGaxvlEC29tDgpib6ocxwp0XL7MlOorV1iKsUeP2LR3L9vOzS3fhzo4GKhfn7VFGJcjR1jWDQCYM4f1k7XRpg2zeD14wNyLfHyElkh83L3LiiXY24sjcLRfP6ZAHz/OKqKJOXKfKJuUFCAqis2TAm35jBrFagT89BMLFHRyYgGC06czX/euXdm8pRnBlMo8LF48HgAwceIiAOJKFWOQCqTRJ+STsAgkEqB6dTa98gpblpLC/ON4K/XNm+xh/vffbAJY8FfjxvkKdePG7KIlDOfOnXyftrffZuWUrRFnZ6BRIzbicfYs8OqrQkskPvjsCK1aicN9p1499jIXFcWCliytCiahy7lzzHhSty7g4SG0NERFkUiAqVOZO8effwKzZgFVqjB3zeBg5tJpiYkDpFIbdOrUFzk5T0VZyrvcCrRSqYS9vT0uX76Mxo0bm0ImQmBcXZnfY6dO7HtuLntwFrRSp6WxdDgXL7J1pFKgTp18hTo4GPDyEkR8iyQlhUVR5+Qwi9DEiUJLZFratWMK9JkzpEAXh1jcNwrSrx+7D+zdy0avLM2aReQjFv96wnhIpWzUMj0dOHECeP4cqFkTWLXKcrMdKRR2WLnyN8TGHhBlKe9yK9ByuRw1atQgN41KhEKRrxQDzD/zwYN8H+qrV5kbyJ07bPr5Z7aen19+UCKfPo+CVYqiUrG83bGxbCRg8WLLtBaUh/btWdT4+fPs+MkdKJ/sbHZdAeJScHr1YikI791jinTDhkJLRBhCwfLdYjq/iIojkwFLlgBz57K87StXsgBgwjQY9Nj65JNP8PHHH+PHH3+Em7VFOBFlIpWyFHm1awMDB7JlCQn5Lh9XrjBFOjaWTX/9xdapUkVXoW7Y0DJLUhubdeuACxdYcvtVq5iLg7XTsCE7zrQ04MYNcfj5ioV//2V+i76+LJhXLFSpwnwpDx5kwYSkQFsm9+8DiYn5hhHCulAogKVLhZaicmCQAr1hwwbcu3cPfn5+CAgIKJKF419K8Frp8PICevRgE8DyF1+7pps+Lz2dZZfg6+/I5UXT51W24KT9+1ngBwAsWMBeSioDNjYsmPDIEWYNIwU6HyHLd5dFv35MgT54EJgyxXKHhisz/PnVogUZMAhxk5OThddfbwiVKgsnTtwFIC5zukEK9KvktEiUgaMjUwD4IUKVilmlebePK1eYj9bVq2ziqVlTt2pitWrml91cXL/O3DUAYMwYoEsXQcUxO+3aMQU6IgIYN05oacSD0OW7S6NVK+aaFRvLAop79xZaIqK8UPo6wlLgOA5xcY+082LDIAV67ty5xpaDsHJkMjbk27AhMHSobvo8fnr4kPlWP3gA/O9/bDt3d6BZMxt4eDSAr68UVaow5dzRkWX9KPjp4GA5PtbPngEffcQq8nXpwhToykZICPu8cYNSo/HEx7PzXyplFnqxIZUCffsCmzYxNw5SoC2LnJz8CqCkQBNix9bWDt9/fwaJiaehUIhvuMvg0J2UlBT8+uuvuH//Pj766CO4ubnh33//hbe3N6pZs9mQMAolpc8r6Ed98yazUh87JgVQX682HRx0FWt+vjiFu/Ayft7OzrRD53l5wIwZzA+xVi2Wt9NSFH9j4u3Njj86mgUThoUJLZHw8NbnRo3E6wvfty+renbhAnsJptu95XD5Msuq5OXFrj2CEDM2NjZo2LAVYmMTYCPCyHqDFOirV6+ie/fucHFxwcOHDzFmzBi4ublhz549ePz4MbZt22ZsOYlKgKsr0LkzmwBmLYmKAi5dUuPBg8fguABkZkqRmcnyW/KfGRms/DXHsWWZmRWTw8amdEW7NOW74KetbdG2OQ5Yvpy5rVSpwoIGC4UQVCpCQpgCHRFBCjRgGcPrPj7MveTsWeCPP4D33hNaIkJfCroHic2/niAsDYMU6GnTpmHEiBFYvnw5qlSpol3ep08fDB061GjCEZUbOzugeXOgWTMNYmOvws+vOqTFmGo5jllVeOW5oHJdUMkuuKy49TIzWYo+tZplh0hLq5j8cnlRpVoqZZY7qZT5P/v7V2wflk5ICLBjB3uwc1zlfqir1cwSD4hbgQZYMCGvQI8ZY/1pF60FXoHm3acIQsyoVCocPrwTyclX0LhxGKyilPeFCxewadOmIsurVauG+Pj4CgtVHElJSZg0aRL++OMPSKVSDBw4EOvWrYNTKeXvunTpgn/++Udn2bhx47Bx40YAwPfff4+RI0cWu+3Tp0/h5eWF48ePo2vXrkV+j4uLgw/VIBYFEglTtu3smM+0oXAcy8FrqPJdcB5gqchSUthUmIkT6SEGsEBRhYK5s9y/z4rxVFaiolg5XkdH5sIhZjp3Zi4mT5+yF0KxK/wEu8bu3WP3SzH61xNEYZTKXHz22QgAwJgxnwGwF1SewhikQCsUCqQVY567c+cOPD09KyxUcQwbNgxxcXEIDw+HUqnEyJEjMXbsWOzcubPU7caMGYMFCxZovzs45NdSf/PNN9GrUL3kESNGICcnB16Fyujdvn0bzgWcEgv/Tlg+vA+1gwNQkdNYrWaKeEnKt7Mz0K2b8eS2ZOzsWDqtiAhmHavMCjRvHWzTRvyFZRQKFkC4ezerTEgKtPjh3YOCgihgl7AMJBIp2rQJRW7us2JHn4XGoNt0v379sGDBAvz8ouScRCLB48ePMXPmTAzkK2sYkaioKBw8eBAXLlxAq1atAADr169Hnz59sHLlSvj5+ZW4rYODQ4mWYnt7e9jb57/RJCYm4tixY/j222+LrOvl5QVXuusQemBjk+8XTZRNu3ZMgT5zBnjrLaGlEQ5L8H8uSL9+TIE+fpyyqFgCVH2QsDTs7OyxYcNfL0p5i8v6DBioQK9atQqDBg2Cl5cXsrOz0blzZ8THxyMkJASLFi0ytoyIiIiAq6urVnkGgO7du0MqleLcuXMYMGBAidvu2LED27dvh4+PD/r27Ys5c+boWKELsm3bNjg4OGDQoEFFfgsODkZubi4aN26MefPmoUOHDqXKnJubi9zcXO133mKvVCqhVCpL3dYYqFTsU6Mx+a5Mjkaj1PkkxIGx+oXlO5bj8mUOWVmqSlmcIyMDuHpVBkCCNm2UFb5uzXHN1K0L1K8vw+3bEvz1lxpvvmmCm00B+TUapcXfA4S6l2k0wNmz/PmlgkYjvpy6QkPPGXHC94darYQZVCcA0FtHM0iBdnFxQXh4OE6fPo0rV64gIyMDLVq0QPfu3Q1prkzi4+OLuEzIZDK4ubmV6nM9dOhQBAQEwM/PD1evXsXMmTNx+/Zt7Nmzp9j1v/32WwwdOlTHKu3r64uNGzeiVatWyM3NxZYtW9ClSxecO3cOLVq0KHHfS5Yswfz584ssP3z4cIkKvLGRSFjBA2shPj5caBGIYqhovygUgIdHDzx75oDw8Ito2TLBSJJZDufO+UCtbgtf3wxIJEeNdt2a+prp1Kkmbt9uij170vHSS/+UvUE5scnJ0c7HxR2C2krersx9L7t/3wUpKV1gZ6eCh8cBxMaSAl0S9JwRJ9euhePaNfPsKysrS6/1KuRp16FDh1ItsU2aNMGBAwfgX0KqgVmzZmHZsmWl7iMqKspg+caOHasji6+vL0JDQ3H//n3ULlQzOSIiAlFRUfjxxx91ltevXx/16+fnIG7fvj3u37+PNWvWFFm3ILNnz8a0adO039PS0uDv74+wsDAdX2pT8fQpcPGideRo1WiUiI8Ph49PD0il4orCrcwYs186dLDB3r3A3btt0LevFQyblJO7d5l/X8eO9vDz61Ph9sx1zbzxBvDDDxwePHBFWlofNGhg3Pal2fk5KX19e0Jjb9k5H4W6lx0+zM6vNm2kqFGDqt8UBz1nxEdOThaGDw+BSpWBv/76F/XqmaeUd3ExfsVh0lCVhw8flmoKnz59OkaMGFFqG7Vq1YKPjw8SEnStUiqVCklJSeXKhNH2RW3ce/fuFVGgt2zZguDgYLRs2bLMdtq0aYNTp06Vuo5CoYBCoSiyXC6XQy43/cXJByGJ0O/eYKRSOd3YRIgx+iUkhAWjnT1rA6m08uVEy/dPNe7xm/qaqVqVVdI8fBj44w85GjY0bvsFZZdK5YCVXP/mvpfx/vUhIVJRBmOJCXrOiAeJRIYHD5gRVSqVmUV3AqD3fgSN9fb09NQra0dISAhSUlIQGRmpVXCPHTsGjUajVYr14fLlywCYW0ZBMjIy8PPPP2PJkiV6t1O4DYIgDKdNG/ay9/AhEBcHVKbL67//2GRjA+jx/i46+vVjCvTBg8CUKcwlhxAPWVmssitAAYSEZWFra4evvw7Hs2dnRVnK2yJeRYOCgtCrVy+MGTMG58+fx+nTpzFx4kQMHjxYm4EjJiYGDRo0wPkXlQju37+PhQsXIjIyEg8fPsS+ffvwzjvvoFOnTmjatKlO+7t374ZKpcJbxaQAWLt2Lfbu3Yt79+7h+vXrmDJlCo4dO4YJEyaY/sAJopLg7Aw0bszmIyKElcXc8NbnZs0sM3NLmzasOmF6OvD330JLQxQmMpIFlVerBlSvLrQ0BKE/NjY2aNmyM5o0aSLKUt4WoUADLJtGgwYNEBoaij59+qBjx47YvHmz9nelUonbt29rnb9tbW1x5MgRhIWFoUGDBpg+fToGDhyIP/74o0jb3377LV577bVi09Tl5eVh+vTpaNKkCTp37owrV67gyJEjCA0NNdmxEkRlhC8sU9kUaH54vRyDaaJCKgX69mXz+/YJKwtRlILp6ypzpU+CMDYiT9efj5ubW6lFUwIDA8Fx+ZHF/v7+RaoQlsSZM2dK/G3GjBmYMWOG/oISBGEQISHApk2ssp1KJf5iIsZApbKc8t2l0bcvsGUL67vYWKCU1PyEmeFfSC35/CIqJyqVCidO7EVSUqQoS3lbjAWaIAjrJigIcHFhOZGvXxdaGvNw/TqrTuniAqNnsDAnfn5A69YAxwHFDPIRAhEbCzx+zPzrW7cWWhqCKB9KZS5mzHgdS5cuRV5ebtkbmBmTKtCbNm2Ct7e3KXdBEISVYGPD/GmB/GFna4d332jThh2/JdOvH/v84w/rKOBkDfDXUePGlulfT1RuJBIpmjYNQYMGDUSZPUbvQdIvvvhC70YnT54MgBUyIQiC0Jd27YDwcDbs/N57QktjeqxpeL1LF6BKFSA+nrlyWKpPtzXBK9B8fAFBWBJ2dvbYsuUfyy/lvWbNGp3viYmJyMrK0gbepaSkwMHBAV5eXloFmiAIojzwiuTNm0BKClBMXK/VkJrKjhOwDgXazg7o1Qv45ReW05sUaGGxFv96ghAretvEHzx4oJ0WLVqE4OBgREVFISkpCUlJSYiKikKLFi2wcOFCU8pLEIQV4+0N1K7NfGl59wZr5cIF5upQqxY7bmugf3/2efw4e0EghOPmTRZP4OzM4gsIgjAuBjmVzJkzB+vXr9cpcV2/fn2sWbMGn376qdGEIwii8sEPN1u7HzR/fNZkqa1fH6hXD8jLAw4dElqayg3vHmQN/vVE5SQnJxvDh4fgww8/RE5OttDiFMEgBTouLg4qlarIcrVajadPn1ZYKIIgKi/8cPPZs8wSbY1wnG5+XmtBIskPJty7V1hZKjv8CI41nV9E5YLjNIiKisS9e/egEWFkskEKdGhoKMaNG4d///1XuywyMhLjx49H9+7djSYcQRCVj+bNWTnoxETg/n2hpTENjx6xYDu53DLLd5dGr17suG7fBm7dElqaykl6en4qSFKgCUtFLldg9erf8emnn8LWViG0OEUwSIHeunUrfHx80KpVKygUCigUCrRp0wbe3t7YsmWLsWUkCKISoVDkK5XWWpWQtz43b86C76wJV1eWkQOgnNBCcf48868PDGRl1gnCEpHJZOjYsQ9atWoFmQgraxmkQHt6euLAgQO4desWfvnlF/zyyy+IiorCgQMH4OXlZWwZCYKoZPBWM2tXoK3J/7kgvBvHX38BueKrf2D1UPo6gjA9FcpMHRgYiPr166NPnz6oV6+esWQiCKKS0749+7x0CcgWX+xIhcjLAy5eZPPWquC0acMyi6SlAf/8I7Q0lYuC/vXW+oJGVA7UajXOnTuCy5cvQ61WCy1OEQxSoLOysjBq1Cg4ODigUaNGePz4MQBg0qRJWLp0qVEFJAii8hEQwIaelUogMlJoaYzL1atATg7g7g7UqSO0NKbBxgbo25fN79snrCyVjcePgbg46/SvJyoXeXk5mDSpD+bNm4fc3ByhxSmCQQr07NmzceXKFRw/fhx2BRz4unfvjt27dxtNOIIgKicSifWms+OPp00bQITVaY3GK6+wz3PnWMAkYR54t6fgYMBefMXbCEJvJBIp6tZtisDAQFGW8jZIot9//x0bNmxAx44dIZFItMsbNWqE+9YaNk8QhFmxVj/oyuKfWr060Lo1cymgYELzwaevI/cNwtKxs7PHjh0XsXbtWlGW8jZIgU5MTCw2WDAzM1NHoSYIgjAUvgDEo0dAbKzQ0hiH5OT81G5t2ggriznggwn/+INlhSBMi1Jp/f71BCEWDFKgW7VqhT///FP7nVeat2zZghC6agmCMAJVqgCNGrF5a7FC89bBevUADw9hZTEHXbsCTk7sBcjafNnFyJUrLOjWzQ2oW1doaQjCujEosd7ixYvRu3dv3Lx5EyqVCuvWrcPNmzdx5swZ/EMh1wRBGIn27VnQ3dmzwMCBQktTcayx+mBp2Nmxwiq//soqE7ZuLbRE1k1B9w0RuowSRLnIycnG5Mm9kJv7HL/91hWAXGiRdDDoEuvYsSOuXLkClUqFJk2a4PDhw/Dy8kJERARaUtgvQRBGglc0z58HVCphZako1lq+uyx4N45jx1haO8J08CM1len8IqwXjtPg339P4MaNG6Is5V1uC7RSqcS4ceMwZ84cfPPNN6aQiSAIAgAQFAS4uACpqaw0cXCw0BIZzv37wLNnrNJis2ZCS2M+goJYur5794DDh4FBg4SWyDop6F9PAYSENSCXK7B48U4kJ1+yjlLecrkcv/32mylkIQiC0MHGJj/YztL9oHnrc8uWTImuLEgkQP/+bH7vXmFlsWYqm389Yf3IZDJ07z4IHTp0sJ5S3q+++ip+//13I4tCEARRFD4u2VoU6Mo4vN67NyCTAVFRwJ07QktjnVD1QYIwLwap9HXr1sWCBQtw+vRptGzZEo6Ojjq/T5482SjCEQRB8Ap0VBSQkgK4ugopjWHk5LCy5EDlVKBdXYHOnYGjR1lKu+nThZbIuijoX0+JsAhrQa1W4+rVM3j2LApNmvSE2IIIDVKgv/32W7i6uiIyMhKRhXITSSQSUqAJgjAanp75PrRnz7KsDpbG5ctAbi7g7Q3UrCm0NMLQrx9ToA8cACZNAmxthZbIeqis/vWEdZOXl4MxY7oAAPr3fw+AXanrmxuDFOgHDx4YWw6CIIgSCQmxbAW64PB6Za011a4d4OUFJCQAJ04A3bsLLZH1UFn96wnrRiKRwN+/DlQqcRbpo0yRBEGIHt7t4exZNlxtaVB6MRYQ+sorbH7fPmFlsTbo/CKsETs7B/z22018/fXXsLd3EFqcIhgc1vjff/9h3759ePz4MfLy8nR+W716dYUFIwiC4AkOZpa1Z8+YJdqSqqwlJrIhdomkcpTvLo1+/YCtW5nCFx8P+PgILZHlU9C/nvyfCcJ8GKRAHz16FP369UOtWrVw69YtNG7cGA8fPgTHcWjRooWxZSQIopKjULDh6TNn2GRJCjSfXiwoyDIDII1J9eqsHyMjgf37gdGjhZbI8rl0CcjLY/71gYFCS0MQlQeDXDhmz56NDz/8ENeuXYOdnR1+++03PHnyBJ07d8brr79ubBkJgiDQvj375P09LQUaXteFr0z4xx+ACIuLWRwF0yOK0E2UIAwmNzcHU6f2x8KFC5GTkyO0OEUwSIGOiorCO++8A4Alus7OzoaTkxMWLFiAZcuWGVVAnqSkJAwbNgzOzs5wdXXFqFGjkJGRUeZ2ERER6NatGxwdHeHs7IxOnTohOzu7XO1evXoVL730Euzs7ODv74/ly5cb/fgIgigdXgG9fBkocAmLGo0m3wJNCjQjNBRwdARiYoB//xVaGstHyPziOTlUnp0wHRqNGqdP/4XIyEhoNGqhxSmCQQq0o6Oj1u/Z19cX9+/f1/727Nkz40hWiGHDhuHGjRsIDw/H/v37ceLECYwdO7bUbSIiItCrVy+EhYXh/PnzuHDhAiZOnAipNP+wy2o3LS0NYWFhCAgIQGRkJFasWIF58+Zh8+bNJjlOgiCKJyAA8PUFlErmAmAJ3LnDclc7OABNmwotjTiwswN69mTzVJmwYiQk5PvXt25t/v0nJrLzW6Uy/74J60cut8Vnn23BpEmTIJeLL++lQT7Q7dq1w6lTpxAUFIQ+ffpg+vTpuHbtGvbs2YN2JngNjoqKwsGDB3HhwgW0atUKALB+/Xr06dMHK1euhJ+fX7HbTZ06FZMnT8asWbO0y+rXr1+udnfs2IG8vDxs3boVtra2aNSoES5fvozVq1eXqcATBGE8JBJmZfvf/5hbRMeOQktUNrx1sFUrVomPYPTrB+zZAxw7BsycCTg5CS2RZcKPbjRsaH7/+txcdk5XqQKkpgLu7ubdP2H9yGRyvPLKO4iNPQC5XFxFVAADFejVq1dr3Rzmz5+PjIwM7N69G3Xr1jVJBo6IiAi4urpqlVwA6N69O6RSKc6dO4cBAwYU2SYhIQHnzp3DsGHD0L59e9y/fx8NGjTAokWL0PHFk1efdiMiItCpUyfYFsj637NnTyxbtgzJycmoWrVqsTLn5uYiNzdX+z3txTiXUqmEUqms2B+iB7xFwBp8DDUapc4nIQ6E6Jd27ST43/9kiIjgoNGI3+wVEWEDQIq2bdXQmPFiFPs1ExQE1KolQ3S0BAcPqvHaayX8NwXk12iUoj0efTF2vwh1fgFAcjLg5sYKHV27BpTwKLQYxH7NVFb4/lCrlTCD6gQAeutoBinQtWrV0s47Ojpi48aNhjSjN/Hx8fDy8tJZJpPJ4Obmhvj4+GK3iY6OBgDMmzcPK1euRHBwMLZt24bQ0FBcv34ddevW1avd+Ph41CxUOszb21v7W0kK9JIlSzB//vwiyw8fPgwHB/PkM5RIgNhYs+zKLMTHhwstAlEM5uyX6tVlkEp74/FjKS5dOg5v7yyz7bu8ZGfb4MqVPgCAWrX+RmxsptllEPM106VLLURHN8Fvv6WhXbsTxa5jUyBwKC7uENR24qpEZijG6BeNBoiI6AVAgTp1ziA2NqnigpWTxEQ22dhYz7NGzNdMZUOtVuPRo0cv5g/i2jUbs+w3K0u/54qgg4qzZs0qM+gwKirKoLb5t/Fx48Zh5MiRAIDmzZvj6NGj2Lp1K5YsWWJQu/oye/ZsTJs2Tfs9LS0N/v7+CAsLg7Ozs0n3DQBPnwIXLwLVqpl8VyZHo1EiPj4cPj49IJWKbxinsiJUvzRpAly5AkRHd0Pz5uIdYjl1SgKVSgo/Pw4tWnQ2a4YES7hm3nwT+OEHDnfvVkVWVh/UqVN0HWl2/kuHr29PaOwdzSih8TFmv9y8KUF6ugyOjhy6dGlnVhehtDRW0KhdO5Zi8uZN4OFDoARvSovAEq6ZykZ2diYGDmRGymvXElC/vqtZ9pumZ2SsQZecVCottayiWq1ftOT06dMxYsSIUtepVasWfHx8kJCQoLNcpVIhKSkJPiVk4vf19QUANGzYUGd5UFAQHj9+DAB6tevj44OnT5/qrMN/L2nfAKBQKKAopqaqXC43iy8PfzOVWlGtSalUTjc2EWLufgkJYQr0uXM2eP1181gkDCE/+4YENjbCnLdivmbc3YFOnYC//wb275ejgL1BS0HZpVI5INJjKS/G6Jfz59ln69YS2Nqa939JTwcaNMj3Xa9WDXj0CFCrARG6qpYLMV8zlQ0bG1t4evpBrc6BTGZrNj9offdjkAL9v//9T+e7UqnEpUuX8MMPPxTrtlASnp6e8PT0LHO9kJAQpKSkIDIyEi1btgQAHDt2DBqNBm3bti12m8DAQPj5+eH27ds6y+/cuYPevXvr3W5ISAg++eQTKJVK7Z8aHh6O+vXrl+i+QRCE6QgJATZuBC5cYL7+Yg3O4wMIqTpcyfTvzxToAweASZMsX/kyJ0Klr+ODBwt6P7q5AR4ezC+6kFckQRiMnZ0D/vzzIWJjD1hPKe/+/fsXWTZo0CA0atQIu3fvxqhRoyosWEGCgoLQq1cvjBkzBhs3boRSqcTEiRMxePBgbQaOmJgYhIaGYtu2bWjTpg0kEgk++ugjzJ07F82aNUNwcDB++OEH3Lp1C7/++qve7Q4dOhTz58/HqFGjMHPmTFy/fh3r1q3DmjVrjHqMBEHoR4MGgIsLi/y/dg1o3lxoiYoSF8cscjY2LAMHUTzt2rEgtMRE4MQJliOaKJvMTDYKA5hfgU5JYcpywawfUing78/Ks3McFXQhKgdGHeBv164djh49aswmtezYsQMNGjRAaGgo+vTpg44dO+rkYlYqlbh9+7aO8/eUKVMwe/ZsTJ06Fc2aNcPRo0cRHh6O2rVr692ui4sLDh8+jAcPHqBly5aYPn06PvvsM0phRxACYWMD8ANPZ84IK0tJ8NbBRo1Ymi+ieGQy4JVX2Py+fcLKYklERjJ3ierV2WQuOI5ZoKtXL+oe6OkJODtTYRWi8mC0wc/s7Gx88cUXqGaiqDU3Nzfs3LmzxN8DAwPBcVyR5bNmzdLJA13edgGgadOmOHnypP7CEgRhUkJCgMOHmaI6YYLQ0hSF3Df0p29f4LvvWG7vhARyAdAHocrDp6ezF0IPj6K/2dkxxfrWLTZCRBAVJTc3B3PmDENOTjy2bu0GQFw+XgYp0FWrVtUJIuQ4Dunp6XBwcMD27duNJhxBEERx8IrprVvM71JM4QhqdX6AF5XvLpsaNYAWLVhZ7/37gXffFVoi8SNUefjUVKBePaYsF4e3N6uMmJNT8joEoS8ajRrHju3RzosNgxToNWvW6CjQUqkUnp6eaNu2LQXWiQQnJ8DeHsjIoCpfhPXh4QHUrQvcvcuUiV69hJYon5s38y11QUFCS2MZ9OvHFOh9+4CRI8mHtjRiYoDHj83vX5+Xx1xuSkk+BVfXfJ/20tYjTItazc4TLy/LfpGRy23x0UfrkJp6w3pKeZeVeo4QHicndgN79IgUaMI6adeOKdAREeJSoHn3jTZtxJshRGyEhgIrVgD//ccU6RdJkYhi4M+vpk3Ne29PTmapB0srGS6RMDeOmBimxNmIN8ukVZOSwl7gnz0zr4+8sZHJ5Hj99fHWVcr76tWreq/btGlTQ3ZBGAFfX5bcXsypvgjCUEJCgB9/ZAqFRiOenOdCpRezZOztgbAw4H//Y1ZoUqBLhj+/SsjgahJKCx4sjIcHc6lKSxOXa1VlgePYyHOdOuz5n5cH2IrPeGsVGKRWBQcHl1pIBWB+0RKJRO+iKoTxcXNj1gI+7RBBWBPBwWx48vlzZomuX19oidiD6/p1Nk8KdPno148p0EeOAB99RCNnxaFSsfzngHkDVHmXJD3KNsDWlina166RAi0EqaksiLN2bSAri90fLdWdRqPR4PHju0hIiEXTpuKrOmuQzWbPnj2oWbMmvvrqK1y6dAmXLl3CV199hdq1a+O3335DdHQ0Hjx4gOjoaGPLS5QDmYwF6GRmlr0uQVgatrb5PqB8VgKhuXCBDV0HBLARIEJ/GjcGatZkls7Dh4WWRpzcuMFe0lxcWD50c5Gaysp06+tP6+XFRhUKZJUlzERqKsvJ7ejIPvPy2D3JEsnNzcagQY3w/vvvIycnW2hximCQBXrx4sX44osv0KdPH+2ypk2bwt/fH3PmzEFkZKTRBCQqhocHu5AyM9knQVgT7doBp06xYW0xhGaQ+4bhSCSsMuHatcyN47XXhJZIfBT0rzeXfzEfPOjtrf82zs7M6vnkCeAgvgJyVktGBnvO8y/vXl5sJDolhfmvWyJOTi7QaJRCi1EsBlmgr127hpo1axZZXrNmTdy8ebPCQhHGo0oVduNLTRVaEoIwPvww9uXL4rB2kQJdMXr3Zorh9essHRqhixDnF698ldcdw8+PxSaoVCYRiyiG5GT2v/PFm+RyNhqWkcF8oy0Ne3tHHDuWiJ07d8LBQXwWQIMU6KCgICxZsgR5eXnaZXl5eViyZAmCKG+T6PD1pRsZYZ3UqMEeGCoVq84mJE+esOwDMhkFwRmKuzvw0kts/o8/hJVFbKSlMRcOwHwKNMcB2dn6BQ8Wxt093/pJmJ6cHKYwF65l5+XFFOr0dGHksmYMUqA3btyIQ4cOoXr16ujevTu6d++O6tWr49ChQ9i4caOxZSQqCJ96iEqsEtaGRJKvTAjtB81bB5s1o2HritCvH/v8809AKc6RW0E4f54ZQmrVKp87RUVIT2fuGIYEodvY5MfgWKL109JISmJuM4VHChwc2AsQjUIbH4MU6DZt2iA6Ohqff/45mjZtiqZNm2LRokWIjo5GmzZtjC0jUUFkMnYB0RsoYY3wbhxiUaDJfaNitG/PXvqTk4XvUzHBVx80Z/q6tDQ2wmNvb9j2np4sm0pGhnHlInRRKtlLir9/8UWIfH1Z0HW2+OLwSiUvLxfz54/CunXrkJubK7Q4RTA4O7CjoyPGjh1rTFkIE+Lpyd5Es7LIOkZYF61bM2vXkyesEIcQhQNUKuDiRTZvzvRi1ohMBrzyCvDDD8wK/YHQAokAjst/mTDX+ZWXx9w2KmLtdnBgCvj9+/l+uYTxef6cPeNLGilwcWFK9H//Gf4yJARqtQp//vmjdl5sGGSB/uGHH/Dnn39qv8+YMQOurq5o3749Hj16ZDThCOPh7MxuhOSPRlgbTk6sKhuQbwU2N9eusaFqV1egXj1hZLAm+vZln2SBZjx6BMTHMx/XFi3Ms0++fkBFczn7+jJFvEDIFGFE1GpmgQ4IKNlPXSJhvtEcZ1luUTKZHJMmLcHw4cMhk4mvEqFBCvTixYth/+I1JiIiAhs2bMDy5cvh4eGBqVOnGlVAwnjwwYSWmhOSIEpCaD/ogtXhxFIR0ZIJDGSFcjTkOwsg//xq3lz/XMwVgeNYUJohwYOFqVqVWUeTk40jG6FLcnL+f1waHh6W1w9yuS3efns6BgwYAFsRllM06NJ48uQJ6tSpAwD4/fffMWjQIIwdOxZLlizByZMnjSogYTzc3dlQDgUTEtYGP6x98aIw2WZ4xZ38n40HH0xImN+/PiODjewYo4KtVMoU8ZwcZsAhjAfHsZGvwEA2OlEaUinzkaZ+MB4GKdBOTk54/vw5AODw4cPo0aMHAMDOzg7ZlualXomQy9mNjBRowtpo0IC5T2RmAlevmnffKSlAVBSbJwXaeHTvDjhYkL+mqcjLy/evN9f5xVceNJa/rKcnGW9MAV+2W18/dS8vZq22lIwcGo0GCQkxeP78OTQi1PoNUqB79OiB0aNHY/To0bhz5462IuGNGzcQGBhoTPkII+PpyW6K9J5DWBNSaX52AnO7cVy4wCxBtWuXPYxK6I+DA9Ctm9BSCM/Vq8xq6O4O1K1r+v3xwYM+PsZrU6Eg440pSE1lvs/6uvXY2jIrtKX0Q25uNl55pSZGjRolylLeBinQX375JUJCQpCYmIjffvsN7i9qREZGRmLIkCFGFZAwLs7O7C2UggkJa0OodHbkvmE6Xn45fz4xUTg5hKSgf31xKcqMjaGVB8vC25sp0jk5xm23ssKX7S7vi46PD3PPsZS0tjY2MtiYq259OTEojZ2rqys2bNhQZPn8+fN1vr///vtYsGABPIzhSEUYBYmEDc09ecKCCUV6XhJEueEV2Fu3WFEBNzfT75Pj8vPzUvo649O4cf78+PHAiq+Yxa0yYc70dXzwYKNGxg+G5V0N4uJYQDtRMZKSWMYfJ6fybefoyDJy3L0r/tSC9vaOiIjIQmzsAesp5a0v27dvR5qljBVUItzdmSXaUt5ACUIfPDzyU8jxSq2pefgQePqUDY0GB5tnn5WJghbX+KfA6NHsBamykJQE3L7N5s1Ro4wPHjSFKxJvvFGrKRNURcnOZtZ8Pz/DtvfzYzFRNBpQMUyqQHNUv1OUWJofFEHoi7nT2Zk7vVhlpl5dloJr3Lj8oDprh38RrFePGT5MjbGDBwvj4cGCfS0liE2sJCUxK76rq2Hbu7oyV46kJGNKVfmgjKWVFE9P8kcjrA9+mPvsWfOkajJ3dbjKzBdfAC1bskwrkyYBf/8ttESmh39BM8f5pVQaP3iwMHI5M97Q6Kfh8AVpqlc33CdeImHbazTCpP3Ul7y8XCxfPhmbNm0SZSlvUqArKS4uTImmYELCmmjWjFmCk5KAO3dMu6+8PCAyks1TAKHpcXJiSnSXLkzZmzkT+P13oaUyHRxn3vzPpgoeLIyXF/PDzcgw7X6slaQk9h9WdETCw4O1IebCKmq1Cr/+uhF//fWX9ZTyJiwfvrSnUklJ1QnrwdYWaN2azZu6rPeVK0BuLnsQ1a5t2n0RDIUCWLoU6N+f3bc+/xz4/numbFob9+4Bz5+zF8JmzUy7L44DsrKMU3mwLJycmJWbjDflR6ViU40aFe8nGxsWkJudLV4dQCaTY/ToT/Hmm29aTylvwjpwd2dRuOQLTVgT5vKDLpi+zhzpxQiGTAZ8+ikwfDj7vmEDsG6d9SnR/Atgy5bsxdCUZGSwZ4G58pjzwW9KpXn2Zy2kpLDsQl5exmnPy4slFBCrDiCX22Ls2M8wZMgQ6ynlrS9vvfUWnJ2dTbkLogJQcnvCGuH9Ra9cYVY1U2Hu8spEPhIJ84P+4AP2fft2YP58cftzlhdz+tenprKgNFMFDxbGzY0ZcMgKrT8aDfP/DwhgL5HGQKFg7VFQp2EY3A0pKSk4f/48EhISipRYfOeddwAAX3/9dcWkI0yOlxcbKszJoSwChHXg78/ck2JiWLaGTp2Mv4/nz/N9rM2RXowonrffZhkFPv8c2L+fGQMWL7b8e1lODnD5Mps39QuaUsleSEwZPFgYGxvmhnDxIhs5oBGcsklNZee6vmW79cXHB7h/Pz+FoZjgOA7p6SnIyMgQZVY3gxToP/74A8OGDUNGRgacnZ0hKXD2SyQSrQJNiB9XV+bD+eyZeW+gBGEqJBKmdPz2G7PimUKB5tOLNWhgnoItRMn07cuGoWfPBk6cACZPBlavFp8yUB7+/ZcFqfr4mL5wTEoKewaY+zz29GRuI+nprP+IkuE49nLYrBmzGhsTJyfmUhMdLb5rJicnC6GhzF/l5s2XAYjLjcMgF47p06fj3XffRUZGBlJSUpCcnKydkkyUWDApKQnDhg2Ds7MzXF1dMWrUKGToEcYbERGBbt26wdHREc7OzujUqROys1lN9YcPH2LUqFGoWbMm7O3tUbt2bcydOxd5fJ6YF+tIJJIi01lTRyiZCT6dTV6eeAMJCKK8mLqsN7lviIvOnZkvtKMjUz7HjmVGAUul4PllSuusOYMHC2Nvz0aKyH2gbHjrsKmMXNWqsVEBEWaKEzUGXTIxMTGYPHkyHBwcjC1PiQwbNgw3btxAeHg49u/fjxMnTmDs2LGlbhMREYFevXohLCwM58+fx4ULFzBx4kRIX9wpbt26BY1Gg02bNuHGjRtYs2YNNm7ciI8//rhIW0eOHEFcXJx2atmypUmOUwg8PKgyIWFdtGrFHgj//ccmY1KwfDcp0OKhRQtg82ZmSb1zh1UtNHbfmwtzvaDxipmHh2n3UxI+Piw3NClupZOczF5yHE1UzbpqVeYaIrbCKnZ2DjhzJhO//vor7O3Np2/qi0EKdM+ePXHRjKWgoqKicPDgQWzZsgVt27ZFx44dsX79euzatQuxsbElbjd16lRMnjwZs2bNQqNGjVC/fn288cYbULwYA+nVqxe+++47hIWFoVatWujXrx8+/PBD7Nmzp0hb7u7u8PHx0U5yufhSqhiKQsHeQCmYkLAWnJzyU38Z2wp99y7zgba3B5o2NW7bRMWoXx/49lt2P/vvP2DUKNZflsTTp2w4XSrNT8loKvjgQTPawnSoWpUp72LORSw0WVnMp9/Qst36IJGw2BG1WlyBuBKJBDKZHDKZTMdVWCwY5AP98ssv46OPPsLNmzfRpEmTIspkv379jCIcT0REBFxdXdGqVSvtsu7du0MqleLcuXMYMGBAkW0SEhJw7tw5DBs2DO3bt8f9+/fRoEEDLFq0CB07dixxX6mpqXArxhmsX79+yMnJQb169TBjxowyjzE3N1enck7aC+1UqVRCKcLcPW5uLLI3O9v4PlYVRaNR6nwS4kDs/dK2rRT//muDiAgNBg5UG63diAgpABu0aKGBTKYWpeuT2PtGbwrIr9Eo9TqeatWAb74BJk+W4d49CcaO5bBqlRrBwcIHIenTL2fPSgDI0LChBlWqmO784h9Dnp7CppPz9QXi4pjiZm43koKI9ZpJSmLKraOjafvJxYW90CQnm6dsvL7w/aFWK812nuqroxmkQI8ZMwYAsGDBgiK/SSQSqNXGe1gBQHx8PLwKJT6UyWRwc3NDfHx8sdtER0cDAObNm4eVK1ciODgY27ZtQ2hoKK5fv466desW2ebevXtYv349Vq5cqV3m5OSEVatWoUOHDpBKpfjtt9/w6quv4vfffy9ViV6yZAnmz59fZPnhw4fN6vpSXp4/F1qCkomPDxdaBKIYxNovtWu7AOiC8+c1ePToL8jlxlGgTpwIAeCFoKAbiI2NNkqbpkKsfaMvNjk52vm4uENQlyO9xrx5cixa1BZRUe6YOFGCGTMuolWrp6YQs9yU1i9//90SQHU0anQHsbG3TS4L744kJBIJUMKj3OyI8Zp58oRN5qKUgX2zolQqsWPHDgDAsGFKXLtmnpH/LD3zn0o4AXODzJo1C8uWLSt1naioKOzZswc//PADbt/WvZl4eXlh/vz5GD9+fJHtzpw5gw4dOmD27NlYvHixdnnTpk3x8ssvY8mSJTrrx8TEoHPnzujSpQu2bNlSqkzvvPMOHjx4gJMnT5a4TnEWaH9/fzx79ky0ubFjY1kATrVq4korpNEoER8fDh+fHpBKrcd1xtIRe79oNEDv3jIkJ0vw9dcqtGxZ8VtdTg7QvbsMeXkS/PKL0uQZEgxF7H2jL9LsTLTszGpLR/6TDI19+ZxAc3KA2bNtcPq0FDY2HObMUaNPH+Es0WX1i1oN9OwpQ1qaBN98o0KzZqaRleNYmsfgYOZbKzTR0cCNG8zSKhRivGbi49kIQfPm5rHO5+bm+9+7uJh+f2WRnZ2Jzi+u/2vXElC/vqtZ9puWlgYPDw+kpqaWqq8ZKR23YUyfPh0jRowodZ1atWrBx8cHCQkJOstVKhWSkpLgU0JYqq+vLwCgYcOGOsuDgoLw+PFjnWWxsbHo2rUr2rdvj82bN5cpd9u2bREeXvpbqkKh0PpaF0Qul4vWf9rbm6UVyswUZ1ohqVQumhsbkY9Y+0UqZUFYf/0FnDsnM4o/6eXL+enFAgPlonrRLA6x9o2+FJRdKpUD5TwWBwdg1SpWZOWvvySYN0+GtDRg6FBjS1o+SuqXqCgWi+LoCDRpIjOZ0pSRwfbh7c2C+ITG15cp0Tk5wvlj84jlmlGpmBEgIMB8bpVyOcvPff06c+cQGltbB7z11jRkZERDoXAwm+6k734MVqAzMzPxzz//4PHjxzpp3wBg8uTJerXh6ekJTz1qh4aEhCAlJQWRkZHa7BfHjh2DRqNB27Zti90mMDAQfn5+RazWd+7cQe/evbXfY2Ji0LVrV7Rs2RLfffedNkNHaVy+fFmroFsTfKDCnTviVKAJorzwCnREBDBxYsXbM1d6McJ4yGRMgXZ1BX76ieWITkkBxo8XXx/y51ebNsarNlccKSlArVrCK6s8zs7spfTJE/HIJDS8L7K5yqvz+Piwl5nMTNNl/dAXudwWkycvRWzsAVGW8jboEr106RL69OmDrKwsZGZmws3NDc+ePYODgwO8vLz0VqD1JSgoCL169cKYMWOwceNGKJVKTJw4EYMHD4bfi9DUmJgYhIaGYtu2bWjTpg0kEgk++ugjzJ07F82aNUNwcDB++OEH3Lp1C7/++qt2my5duiAgIAArV65EYmKidp+8ZfuHH36Ara0tmjdvDgDYs2cPtm7dWqabh6Xi7c2qEuXlASI8XwmiXPBpwG7fZv79FQ2OofzPlolUCkybxqxqX30FbN3KlMiZM1m6Q7HAn18l2IWMAl95UGw2ID8/4PFjZnk15cuDJaDRsID+Ro3M/184O7Nz4+FD4RVosWNQ10ydOhV9+/bFxo0b4eLigrNnz0Iul+Ott97CBx98YGwZAQA7duzAxIkTERoaCqlUioEDB+KLL77Q/q5UKnH79m0d5+8pU6YgJycHU6dORVJSEpo1a4bw8HDUrl0bABAeHo579+7h3r17qF7IEayga/jChQvx6NEjyGQyNGjQALt378agQYNMcpxCw6cVSklhZb4JwpJxdwfq1WOjKufOAX36GN6WOdOLEcZHIgHefZf5di5dCuzZw9K4LVwoDmNBRgZw9Sqb5wsBmYLUVJZ1SQxD9AVxd2ejBHxlxMpMSgo7T41dtltfqlVjLzNCG9I4joNKpYRKpbKeUt6XL1/Gpk2bIJVKYWNjg9zcXNSqVQvLly/H8OHD8dprrxlbTri5uWHnzp0l/h4YGFjsHzxr1izMmjWr2G1GjBhRpg/28OHDMXz48HLJaslIpSyoJD6eBZqIbYiTIMpLSAhToCMiKqZA89kKGjYUR4ANYRgDB7L+mzMHOHqUFZBasUJ4a1tkJAsi9PdnCowp4Dg2NN+ggbgs7wCztAYEsEB2d/fK++zhOHZONmsmnPLq5sYMaM+ema76oT7k5GThpZdYffGbN5NhFaW85XK51lfYy8tLG5Tn4uKCJ+bMtUKYBA8PVohCj0rpBCF6eGve2bMVK1dP7hvWQ/fuwLp1zN/2/HnmD52SIqxM5ji/MjPZvd3cfrX64unJXmQyM4WWRDjS01kwv5AuNlIpe5FTKtlLHVE8BinQzZs3x4ULFwAAnTt3xmeffYYdO3ZgypQpaNy4sVEFJMyPvT3zR0tNFVoSgqg4zZqxczo5mVmiDUGtpvLd1kabNsDXXzO3gZs3WdVCIXMRm0OBTkkRtvJgWTg6smeP0C8zQpKaypRXofvI0zO/sIpQ2Nk54OjRBGzfvt16SnkvXrxYm4Vi0aJFqFq1KsaPH4/ExES90sAR4sfbmw2hibBoIkGUC7kc4IuYGlrW+/Zt9mBzdATIRmA9NGoEbNnC7nePHjEl+sED88vx338sA4WNTf65amz4Es1CDsnrg68ve/YUSu5VKcjMZNmwxBDgKZczl5qsLOZWIgQSiQRVqrjCyclJlKW8DVKgW7Vqha5duwJgLhwHDx5EWloaIiMj0axZM6MKSAiDmxvzQ6vMlgDCeuDdOAxVoHnrYOvWlCHA2ggMBL79FqhZkwWKjh7N8uCaE/78atbMdL7YKSnsnu7mZpr2jYWbG7N+VsZnT3IyU57FEmPB14ZISxNaEnFicJp2lUqFI0eOYNOmTUhPTwfACpJkkOOsVcAHE2ZnC/f2SRDGglegr1wxzL+S/J+tGx8f4JtvmEU6NZX5RPN9bg5Mnb6ODx709xdf8GBh+GdPTk7levbk5rK+EUNlSB57eyaPUO6cSmUeNm9egJ9++qlIvRExYJAC/ejRIzRp0gT9+/fHhAkTtPmTly1bhg8//NCoAhLCQQEdhLXAZzZQq4GLF8u3bWYmU7wBUqCtGVdX5hPdti0zHEyZApRRcNYoqFTAi5Aik6Wv44tiWEp6OE9Plo+4Mlk+nz9nFl+xjRD4+jK3kgIZgs2GSqXEli2fY/fu3VCpxOdPapAC/cEHH6BVq1ZITk6Gvb29dvmAAQNw9OhRowlHCIuDA7t4KuNQGmF9GOrGwacXq15dXNYhwvg4OABr1gA9ejDF9uOPgRd1t0zG9etMwXVxAerXN80+UlJYcJ7Qqfr0xc6OvfBWFgWaL9vt7y++9H0uLkwPECKY0MZGhkGD3kPv3r1hYyM+3zmDFOiTJ0/i008/LVJaMTAwEDExMUYRjBAHPj7sguYDUAjCUjFUgebXN2VxC0I82NoCn38ODBrEXAiWLmWBhqZyJyjovmEK9wqVit3DxR48WBgfHxbIlpMjtCSmJymJWd3Fml6Qz0tu7qQCtrYKzJjxBcaNGweFQmHeneuBQQq0RqOBupjkgP/99x+qVKlSYaEI8eDmxiayQhOWTqtWTEGJiWEZD/SFT19nyvLKhLiwsWFlvseMYd83bgRWrqxYHvGSMLV/fUoKS0cmNteAsnBxYcU8hEyjZg7UavaSUKOGeP3T3d2Zcp+UJLQk4sIgBTosLAxr167VfpdIJMjIyMDcuXPRpyKlvgjRwQc1kB80Yek4OrIsB4D+VuiYGFbS1pTpxQhxIpEA48YBfFjP7t3AZ58ZdzQuNZXloAZMp0BnZopbOSsJiYQ9e1Qq6y7mwb/geHkJLUnJSKXsHMrLs+6+KC8GKdCrVq3C6dOn0bBhQ+Tk5GDo0KFa941ly5YZW0ZCYPhgQkqwQlg65XXj4K2DTZuyCm5E5WPwYObSYWMDHDwITJvGggyNwYULzKpdq5ZpFKiMDMsKHiyMhwcL7rTWol4cx/ooIEC4st364uVl/r7Izs5ESIgDBg4ciKws8VnxDFKgq1evjitXruDjjz/G1KlT0bx5cyxduhSXLl2Cl5hfowiDcHRk/mjWehMjKg+8An3xon7+fKZOL0ZYBr16AatXAwoFcOYMMGGCce6HpvavT0lh925LCR4sjFzOAuteZMq1OtLSWLYRS/BPl8uZFTo93bzpBdVqVbEuw2LA4LBGmUyGt956y5iyECLG1xd4+JANp1EhCcJSqVeP+YImJbHUdKW5ZRRML0bp64gOHYCvvmLp7a5eBcaOBdavN9xyzHGm9X/mgwf9/Izftjnx8mLZUfhUfNZEairQsCHLt2wJ+PiwkbiMDFZgxdQoFPbYv/8Bnj49Bjs78f1JBqtCsbGxOHXqFBISEqApFFkxefLkCgtGiIuCwYSWOhxIEFIpsyb/9Rez/pWmQN+8yR4ULi5AUJD5ZCTES7NmrODKpEnA/fusauGGDcwyV14ePmSVD21tgebNjS6qxQYPFqZKFaa4PX5sXQp0RgZTnMVQtltfHByYX/qdO+ZRoKVSKby8qkGlcodUanDdP5NhkAL9/fffY9y4cbC1tYW7u7tOjXKJREIKtBUik7EL59IlUqAJyyYkJF+BnjSp5PX44fU2bSwvAIswHXXqsNLfEyawbC6jRwNffAE0aFC+dnjrc/PmLO+xscnMZHmlreHc9fMDHj2yrhHQlBTm++7sLLQk5cPPD3jwgGUOMcV5a0kYpNLPmTMHn332GVJTU/Hw4UM8ePBAO0VHRxtbRkIkUGVCwhrgh8vv3GHVv0qC0tcRJeHnx5To+vWZO9C4ceWvcGlK9w0+eFCseYXLi7s7m6wlnWpODnsR4PMrWxJ8YZXS7p3GQqnMw48/rsL//vc/6ynlnZWVhcGDB4vSpE6YDicnVmqUggkJS8bNLb/iG6/EFCY9nVWIA8j/mSgeNzdg0yagRQtmVJg8GTh+XL9t8/JYhUvANAGEqamWHTxYGBsbFkyYmWneADZTkZTE+qdqVaElKT98ekGOM32BNZVKifXrZ+OHH36wnlLeo0aNwi+//GJsWQgLwNeXpV2iyoSEJVNWOrvz59l5XrOmZUTIE8Lg5MQCCbt0YUrxjBnA3r1lb3fligQ5OcwdrnZt48qkUjHlxpJ8a/XB05P53Vp6OlWlkt1bqlcXX9lufTFXYRUbGxlefvltdO3aVZSlvA2SaMmSJXjllVdw8OBBNGnSBHK5XOf31atXG0U4Qny4u+fngnR3F1oagjCMkBDg+++ZBVqjYcGFBTF1dTjCelAoWLnvxYuBffuAhQuZq8Hw4SVvc/Ys05zatTO+EpWSwqzj1nZ/dnBgrjP37pkngM1UJCWxzCKWHEtkY8MCZ/k85qZyRrC1VWDu3G8RG3tAlKW8DVagDx06hPovxkELBxES1gsfTHjlivXdoInKQ9Om7IGckgLcvq2bZcPU6cUI60MmA+bMYcaFbduYVTolhbl1FPdIPHeOaRymOL+sKXiwML6+LIAtN5e9uFgaajUbqbDEypCF8fJi/tCpqZbpimIMDFKgV61aha1bt2LEiBFGFoewBPi8nFlZ7JMgLA25nKWwO3GCuXEUVKAfPwbi4tg6LVoIJyNhWUgkTGGuWhVYtw748UemRH/yiW7miJQUBe7cYVp1mzbGlcHSKw+WhasrO7akJBaPY2lYQtlufbG1ZRUUr16tvAq0QYZ3hUKBDh06GFsWwkKoUoXdvKwlIpqonPB+0IUDCfnvwcGWU+CAEA9vvw3MncssjH/8AcycybIu8Fy+zFJjNGhg/BzNfPCgtZadl0pZMGFuLnMdsCQKlu0u5PVqsXh7MyOaqfzSs7Mz0a2bJ4YOHWo9pbw/+OADrF+/3tiyEBYEH0wo0gqbBFEmvAJ95YruA4DKdxMVpW9fYPlyZqX75x9mmebPscuXmfnR2O4b1ho8WBgPD5Y7OS1NaEnKR2oqc3mwpqBkJyfm0pmcbLp9ZGSkIisry3Q7qAAGuXCcP38ex44dw/79+9GoUaMiQYR79uwxinCEeHF3z/d/svRKV0TlpHp1Nv33H8vh26ULi5A3ZXoxovLQuTPzhZ42Dfj3X1b6e+3afAu0sRVoa6k8WBYKBbNC37jBXDoshbQ0oHFj6ys+4uvLqmqaorCKQmGPX3+9gYSEf0RZytsgC7Srqytee+01dO7cGR4eHnBxcdGZCOtHLmfKh6WnFCIqN4XT2V29ynz73dyAunWFk4uwDlq2ZLmi3dxY4Z6335YhJcUO9vYcmjUz7r4yM1lwmrVU6isNb2+mSGdnCy2JfmRkMFcHa7I+81StyvrDFCntpFIpatSoCz8/P1HWHTHoUvvuu+/0Wu/06dNo1aqVKNOPEBXHy4u9cWZnk68oYZmEhAC//MLcNgpm32jb1nSpmYjKRYMG+aW/Y2NZ8GDLlhzkcuNlrMrMtO7gwcI4OzOlLTbWMp49ycks37clp98rCb6wSkyMdZVa1weTPiJ69+6NmJgYU+6CEJAqVZgSTcGEhKXSqhW74cfEAE+eUPo6wjT4+zMlunZtVkavUyfjRsClpDCF0lqDBwsjkbAy2JZQ1CsnJ3/E1lrx9GRuncb2hVaplPjll69x4MABKJVWUolQXzhrqLlJlIhEwhLbq1QUTEhYJg4O0A6l//UXcOsWm6cAQsLYeHoCW7eqsHDhafTta7xnIx886OdntCYtAg+P/KJeYiYpifWNJflrlxe+sEpWlnGzoyiVeVix4gNs3rwZSmWe8Ro2EjRISVQId3fLjIgmCB7eD3r7dqaI1K1beYbCCfNibw80afLMqEU0UlOZcmbtwYOFkcmY0ibmOBylkt1TqlWz3LLd+uLtzRILGFMXkEpt0K3ba2jfvj2kUvFVnrEYBTopKQnDhg2Ds7MzXF1dMWrUKGToceVERESgW7ducHR0hLOzMzp16oTsApEHgYGBkEgkOtPSpUt12rh69Speeukl2NnZwd/fH8uXLzf68VkqtrZseDI9XWhJCMIweAWavy2Q+wZhSWRkVJ7gwcJ4ejLfb7Eq0c+fW37Zbn1RKNh5aEwFWqGww9KluzBjxgzYiTB9icUo0MOGDcONGzcQHh6O/fv348SJExg7dmyp20RERKBXr14ICwvD+fPnceHCBUycOLFINOeCBQsQFxennSZNmqT9LS0tDWFhYQgICEBkZCRWrFiBefPmYfPmzSY5TkvEy4tdPAWLBRCEpVC3rm5ZelKgCUuBDx709BRaEmFwcmJp1MQYh6NSMQt0jRqVJyDZx8e0hVXEhknfWSVGGrOIiorCwYMHceHCBbRq1QoAsH79evTp0wcrV66EXwnOX1OnTsXkyZMxa9Ys7bL69esXWa9KlSrwKSG/zI4dO5CXl4etW7fC1tYWjRo1wuXLl7F69epSFfjc3Fzk5uZqv6e9eC1TKpWidIavCPb2TAFJTDRNeVWNRqnzSYgDa+qXNm1s8NdfUigUHJo2VVlclbPCWE3fFJBfo1Fa/PEYu19SUlhwmkLBlLXKiKcn8OABq05YkQp/xu6b5GTmWuPqWnn6xs6O6QCPHjFF2hjw/aFWK832P+qro5lUgTZWEGFERARcXV21yjMAdO/eHVKpFOfOncOAAQOKbJOQkIBz585h2LBhaN++Pe7fv48GDRpg0aJF6Nixo866S5cuxcKFC1GjRg0MHToUU6dOhezFeFhERAQ6deoEW1tb7fo9e/bEsmXLkJycjKolFIFfsmQJ5s+fX2T54cOH4WCsM0uExMaaru34+HDTNU4YjDX0S7NmPvjrr7Zo3jwOz59fEFoco2HpfWNTYFgrLu4Q1CIcxjUEY/bLkydsquwkJhqnHWP2TW4uEG7Zl6DBGEMXyM3Nxfjx4wEAX3/9Na5dM09KZH0rH5pUgU43kmNsfHw8vLy8dJbJZDK4ubkhPj6+2G2io6MBAPPmzcPKlSsRHByMbdu2ITQ0FNevX0fdF1USJk+ejBYtWsDNzQ1nzpzB7NmzERcXh9WrV2v3XbNmTZ22vV+YWePj40tUoGfPno1p06Zpv6elpcHf3x9hYWFwdnY24F8QN3l5+cUojF1LR6NRIj4+HD4+PSCVVsDEQBgVa+qX114DAgNVqF3bEy4ufYQWp8JYS99IszO1876+PaGxdxRQmopjzH5JSmKW57ZtK6f/c0FiYli1x+rVDQ/WM2bf8JlB2rVjfVSZ4DjWF8+eVXxEOjs7E0kvKrQ0bNgV9eu7VlxAPUjT05HboMuuefPmxbpnSCQS2NnZoU6dOhgxYgS6du1aajuzZs3CsmXLSl0nKirKEBGheTEGO27cOIwcOVIr99GjR7F161YsWbIEAHSU3KZNm8LW1hbjxo3DkiVLKlQARqFQFLu9XC4vUvrcGpDLWTDhzZusMpEpkErlFq0MWCvW0i8FBrisBkvvm4KyS6VywIKPpSDG6JesLOa/bwmFREyNtzfLBpWZyT4rgjH6Ji0NaNq08uTlLkxAAPD0KUtpV5GXOzu7Kti+/TwSEk7BwaGK2XQnffdjkGt7r169EB0dDUdHR3Tt2hVdu3aFk5MT7t+/j9atWyMuLg7du3fH3r17S21n+vTpiIqKKnWqVasWfHx8kJCQoLOtSqVCUlJSib7Lvr6+AICGDRvqLA8KCsLjx49LlKlt27ZQqVR4+PAhAMDHxwdPnz7VWYf/XtK+KyteXiwrBwUTEgRBmI7MTKY4V9bgwcLY2zPrsxhyQmdksMDOyqweeHoyQ1pFgzttbGxQr14watWqBRtj5n40Ega9Gzx79gzTp0/HnDlzdJZ//vnnePToEQ4fPoy5c+di4cKF6N+/f4nteHp6wlOPO0BISAhSUlIQGRmJli1bAgCOHTsGjUaDtiVUPAgMDISfnx9u376ts/zOnTvo3bt3ifu6fPkypFKp1mUkJCQEn3zyCZRKpfatJDw8HPXr1y/RfaOy4urKLpzExMp98yAIgjAlqalsxM8aS0Mbirc3cP8+M+AI6SqfnMxGBiqr9RlgVueAACAykiUYsNYc2AZZoH/++WcMGTKkyPLBgwfj559/BgAMGTKkiPJqKEFBQejVqxfGjBmD8+fP4/Tp05g4cSIGDx6szcARExODBg0a4Pz58wCYO8lHH32EL774Ar/++ivu3buHOXPm4NatWxg1ahQAFiC4du1aXLlyBdHR0dixYwemTp2Kt956S6scDx06FLa2thg1ahRu3LiB3bt3Y926dTquHwSDL6+al2fcakQEQRAEQ6Vi99cXg6zEC3gDjpAp7bKz2ShstWrCySAWjFFYRaVSYv/+bTh69Kgos5cZZIG2s7PDmTNnUKdOHZ3lZ86c0Sa71mg0Rk18vWPHDkycOBGhoaGQSqUYOHAgvvjiC+3vSqUSt2/f1omenDJlCnJycjB16lQkJSWhWbNmCA8PR+3atQEwP+Vdu3Zh3rx5yM3NRc2aNTF16lQd5djFxQWHDx/GhAkT0LJlS3h4eOCzzz4rMwd1ZcXDg/mgpacbP5iQIAiissNXHiyYu5xgBpzq1VlAoVoNo1Z71JekJDYyQM8+NgpQvTqLizL0/1Aq87BgwWgAwPjxCwCIK4OZQQr0pEmT8N577yEyMhKtW7cGAFy4cAFbtmzBxx9/DAA4dOgQgoODjSaom5sbdu7cWeLvgYGBxabNmzVrlk4e6IK0aNECZ8+eLXPfTZs2xcmTJ/UXthKjULC371u36CZCEARhbDIzgTp1KPNGcXh45Fs9ze1hmZfHPiuSCcTa8PVlObqzsgzLCy2V2qBDh97IyUkQZSlvgy7BTz/9FDVr1sSGDRvw448/AmAFSr755hsMHToUAPDee+9p8/cRlQsvL+aLlptb+VL4EARBmIqsLAoeLA1bW1b579o18yvQSUnMbYFGBvJxdmbxUIYWVlEo7LBmzV7Exh4QZSlvg99hhw0bhmHDhpX4uz3l1qm0VK3KbiL8DYUgCIKoOMnJFDxYFl5ezH3AUKunIahUbKpMZbv1pXp1VugnL4+94FgTBnd1SkqK1mWDT3T977//IiYmxmjCEZYJ74uWm8uSqhMEQRAVQ6Vi99MXcfNECfBWT3MGE6akAG5uNDJQHPz/8kJNtCoMskBfvXoV3bt3h4uLCx4+fIjRo0fDzc0Ne/bswePHj7Ft2zZjy0lYGB4ezEqSnl7xxPYEQRCVnbQ0Ch7UFz8/4PFj9tJhal9xjYZZu4OCyC+9OKRSZpmPjy9/cGdOThaGDGkGlSoTx45FARBXYJVBFuhp06ZhxIgRuHv3ro5fSp8+fXDixAmjCUdYLnZ27CYmhsT2BEEQlk56OhvZIyWtbNzdmeXTHFbo1FQWuEjuiiVjaGEVjuPw5Mk9xMXFFZskQmgMUqAvXLiAcePGFVlerVo1xMfHV1gowjrw9mY3ez46mSAIgig/vD/vi/peRBnIZMxXPDPTtG6EHMdGBgICKGC+NORy9h9lZJSvP2xt7fDNN8exZMkSKBTiCyI0SIFWKBRIKyY79p07d/SqLEhUDvhgQiET2xMEQVg6KSnMIEHBg/rj5cWqAWZmmm4fGRlsH1R5t2y8vPLdOvXFxsYGzZq1R1BQkChLeRukQPfr1w8LFizQVoaRSCR4/PgxZs6ciYEDBxpVQMJykUrZkGNODgUTEgRBGIJKxXxHqfJg+XBwYG6EpjTg8FlRHB1Ntw9rwcGB/VfWZFAzSIFetWoVMjIy4OXlhezsbHTu3Bl16tSBk5MTFi1aZGwZCQvGw4O9oWdkCC0JQRCE5cEXBfHwEFoSy8PXlxlyTOFGmJWVH+tD6IePD3N1yc7Wb32VSoUjR37F6dOnoVKpTCucARgUjuDi4oLw8HCcPn0aV65cQUZGBlq0aIHu3bsbWz7CwrG3ZzeYe/do+JEgCKK8pKcDzZpR8KAh8C8eycnGD/JLSmJ+vVRxV39cXdlLzZMnrGJxWSiVufj4Y1acb+TIjwGIq76IwZfk0aNHcfToUSQkJECj0eDWrVvaUttbt241moCE5ePtDURHA0olCyYgCIIgyoYPHqTQIsOQSpnbQHw8SzdnrCInubn5LopE+ahWjSnQ+ugDEokULVp0Qm7uc0hFWKHGIAV6/vz5WLBgAVq1agVfX19IqPA7UQpubvnBhPQgIAiC0I+UFKakUS59w/H0ZP8fn0fbGCQlMXcEysldfjw88gurlDUqYGdnj40bj7wo5S0u6zNgoAK9ceNGfP/993j77beNLQ9hhfBv6hcvsmBCet8iCIIoHbWaWU0peLBiKBTs+XPzpnEUaD6o09+fnmWGYKpRASEwSPS8vDy0b9/e2LIQVgwfTGjKlEIEQRDWQmoqVR40Ft7egK0tywhVUZKTWZ/QaKrheHkZVlhFbBikQI8ePVrr70wQ+uDgwCwpln7BEARBmIOMDGY5pbiRisNXCkxOrlg7fNnugIDylaQmdLG1ZeW909NLT3Gbk5ONYcNaYcqUKcjJ0TN1hxkxyIUjJycHmzdvxpEjR9C0aVPIC13hq1evNopwhHXh4wM8eEDBhARBEKWRnc1SpJGV0zhIJCx4LSaGuV8YqvympLCYHirbXXG8vfNT3JaUoYvjNLh79yoAQKPRmFE6/TBIgb569SqCg4MBANevX9f5jQIKiZJwc2NTairlNCUIgiiJ5GSm8FHwoPHw8GAuMamp7DlUXjiOWUyDg5kFlagYjo5shOXOnZIVaFtbO6xffwDPn58XZSlvgxTov//+29hyEJUAGxsWPBAZyXzI6F2LIAhCFz54kAp0GBe5nClsV64YpkCnpzNFj8p2Gw9fXzYqnZPDRlwKY2Njg7ZtuyM2Ns96SnkThKF4eLA3TwomJAiCKEpaGvPZpeBB4+PtbfjzJyWFGYAcHIwuVqXF1ZW9kCQlCS2JYZACTZgVR0d2wVAwIUEQRFHS0yl40FQ4ORn2/MnMzK+qSxgPiYSd6xoNSw9YGJVKhVOnDuDixYuiLOVNCjRhdvi8piK8HgiCIASDDx708hJaEuuFV4LL8/xJTmbbkU+68fHwyC+3XhilMhfTpr2Kzz//HHl5ueYXrgxIgSbMjrs780EjKzRBEEQ+yclMeSZFzXTwwez6prTLyWHxO9WqmVauyoqNDUtpl53NLNEFkUikCApqiTp16oiylLf4JCKsHj6YkPygCYIgGBQ8aB54hS0rq/QcxDx8yWlDAg8J/fDyYn7/qam6y+3s7PHDDxFYuXKl9ZTyJkyDWq2GUqkUWgyzUKUKs7JkZekTlKGETCYDkANAbXrhCD2xxH6RAxBfNDdB8MGDlOLT9Hh6smdQenrp1n6Vir3U1KhBWaNMiULB/uOrV1mFQkuBFGgRwHEc4uPjkVLJfBo8PdkNSlbGWchxHHx8fGBj84TyjIsIS+sXjmOTRuMKwAeA+GUmKg9paUDTphQ8aA7s7ZlLxp07pSvQSUnsOUUvNabHxwe4f58VVnFyEloa/SAFWgTwyrOXlxccHBwsQhkxBkol83uysSnr7V4DlSoDMpkTyOtITFhWv3Ach9zcLDx7lgA20OMrtEgEAYDdB+3tqfKgOfHxAaKjgdxcZgEtjFrN/J+bNKGy3ebAyYm5L0VH5yvQOTnZeP/9UOTlJeP337uCjSCKB1KgBUatVmuVZ/dKlviTv2lpNGVZoTWQSvMgl9vBEhS1yoPl9QvvR/f0aQI0Gi+QOwchBlJSWHYiCh40H1WrsheWZ8+KL47Cl+2mjCjmo1o14PHj/MIqHKfB1asRAKyolDdhPHifZ4dKmJ1dImElUbOyhJaEqEwoFA4vRjyUIAWaEBq1mrmy+fmRn6054XMQx8YWzf7AccyVoF49KtttTqpWZS8sT5+yF0q5XIHly39BUlIkbG2LGSYQGMswGwFISkrCsGHD4OzsDFdXV4waNQoZGRllbhcREYFu3brB0dERzs7O6NSpE7KzswEAx48fh0QiKXa6cOECAODhw4fF/n727FmjHl9lcdsojEwGSKVFb2AEYSrYNSy0FATBSEtjFdnIz9b8eHqywM20NN3laWlsNMDbWxi5KisSCcvQxb9UymQydOnSH+3atXsRsC4uLEaBHjZsGG7cuIHw8HDs378fJ06cwNixY0vdJiIiAr169UJYWBjOnz+PCxcuYOLEidp8gu3bt0dcXJzONHr0aNSsWROtWrXSaevIkSM667Vs2dJkx1qZsLFhQTNqS0niQBAEYUSo8qBw2Noyha2wAs2X7bYXX+Y0q8fDw3LqRFiEAh0VFYWDBw9iy5YtaNu2LTp27Ij169dj165diI2NLXG7qVOnYvLkyZg1axYaNWqE+vXr44033oDihfOtra0tfHx8tJO7uzv27t2LkSNHFrEIu7u766wrp7tdmfAW/rKyi1jKX3nixHE4OZV+PE5OEvzxx+9mk6k0evXqghkzpggtBkEQJZCTw2JBKHhQOLy8mL/ti4FpZGYCjo6Uj1soZDIgIIC5dqpUakRG/oNr165BLUIrm/hs4sUQEREBV1dXHatw9+7dIZVKce7cOQwYMKDINgkJCTh37hyGDRuG9u3b4/79+2jQoAEWLVqEjh07Frufffv24fnz5xg5cmSR3/r164ecnBzUq1cPM2bMQL9+/UqVOTc3F7m5+aUn01684iqVSp1cz0qlEhzHQaPRiNJJviLwx1PWsUmlzBJdUko77kW2e47jIJEI+R9pCnyWJkdZv5uHnTt/ffGiZxpZxNMv5UXz4pqzXh9odmz5nxZLAfk1GqXFH0/hfklKYgFs9vZAJSkBIDocHNgLTEwM64DUVCUCA5lSTX0iDG5uLE93QkImxo/vAQB49dVxUCrNc7/Wtx6HRSjQ8fHx8CoUCiuTyeDm5ob4+Phit4mOjgYAzJs3DytXrkRwcDC2bduG0NBQXL9+HXXr1i2yzbfffouePXuievXq2mVOTk5YtWoVOnToAKlUit9++w2vvvoqfv/991KV6CVLlmD+/PlFlh8+fFgnYFAmk8HHxwcZGRnIy8sr/Y+wMLJeRAemp6frXYaztPNWpUovc/u8vDzYmijqQ63O0sqhVJZ8PGp1NpTKtBJ/ryhKpVKvEZAqVWQAOJPKAujXL+XBlH0IACpVHtTqbCQknIBKpTLZfsRAfHy40CJUCJucHO18XNwhqO3sBJTGeBTsl9hYNhFiIRwPHwIPHwotB5Gbmwt/f38AwM2bf+P+ffMEEmbpmdlAUAV61qxZWLZsWanrREVFGdQ2b/EcN26c1qLcvHlzHD16FFu3bsWSJUt01v/vv/9w6NAh/PzzzzrLPTw8MG3aNO331q1bIzY2FitWrChVgZ49e7bOdmlpafD390dYWBicC+QqysnJwZMnT+Dk5AQ7C3s45ObmYsaMGdi9ezfS0tLQqlUrrFq1Cq1btwaQn1nk6tWr+OSTT3Dnzh0EBwdj8+bNaNy4MQDg0aNHmDRpEk6fPo28vDzUqBGIRYuWoWfPPgCAGzeu49NPZ+DMmVNwcHBEaGgPLF26Gh4vIm569eqGhg0bQSaTYdeuHWjUqAl8fHygVquxbdsuraxKpRK1a1fD0qUrMXToO9BoNFi9ejm+++4bPH0ajzp16mHmzE8wYMAg7TaHDh3AzJnT8N9/T9C6dTsMG/Y2AEAmqwK5vOR8UzY29trf//vvCWbP/hDHjoVDKpWiffuOWL58LQICAgEAkZEXMG/eJ7hy5TJUKiWaNAnGsmWrEBzcQtuek5MN1qzZgPDwgzh+/Bg++OBDAMD+/XsxefJULFw4FykpyejRoxc2bNiMKlWqaP+bpk2bYfnyNQCAhg1rYeTIMYiOvof//e9XuLpWxYwZH+Pdd/NjCc6ePYOpUyfizp1baNiwMWbM+BhDhgzEmTORaNo0WOc4OY6DSpUOtdoWixbNw88//4TExARUr+6P6dNnYvjwUdi+/XvMnDkNMTFJ2u3++ON3DBkyEBkZbEhu0aL52L9/L8aNex8rVizB48ePsG7dV1i8eAHu3Hms8/L15puvws3NHV9//a32P1iyZCFu3boJX18/DB36DmbM+LjUgBONJgc2Nvbw8uoEwLKuOX3RaJSIjw+Hj08PSKUW4iNVDNLsTO28r29PaOwdBZSm4hTsl/R0OSQSoF07yvQgNCoVcPasEmlprG+aN5dToLHAZGQAZ88C27a9gtTUcDRp0gP+/ua5l6UVdoovAUEV6OnTp2PEiBGlrlOrVi34+PggISFBZ7lKpUJSUhJ8ikvgCMDXlxVJaNiwoc7yoKAgPH78uMj63333Hdzd3ct0zQCAtm3bIjy8dMuOQqHQ+loXRC6X61gP1Wo1JBIJpFKpVlHgOOFSuzk46J9KadasWdizZw9++OEHBAQEYPny5ejduzfu3bsHNzc37fHMnDkT69atg4+PDz7++GP0798fd+7cgVwux6RJk5CXl4cTJ05AInHE9es34ejoDECKlJQUvPxyd4wYMQqff74AKpUN5syZjXfeGYwDB45p5di5cxtGjx6PI0dOAwCio+/h7bdfR0ZGFpxeZGQ/ciQc2dlZ6Nt3IAApVq5cgl27tmPduo2oXbsuTp8+gdGj34GHhzdeeqnz/9m77/Cmyi+A49+s7pZSCi2jWPZStkAZP0A2iuICBRkiKAgCIkNElqggIoiKoiACCoLiwFHAirI3CCJ7t4yW3T3SJL8/Xpq2lFGgbW7S83me+5Bxk7zJLe3Je897DqdPR9Gt21O8+OJAnn/+Rf75ZwejR7927RX13Hr5gLrfbDbz2GMdaNgwjFWr1mM0Gpk69W06d+7I1q3/4ubmRnx8It2792batPrYbDY+/vgDnnjiEfbsOWIPhAHeffct3nprCu+9NxOj0cjChfM4ceIYv/76C99//xtXr16hZ88ufPDBVCZMeCfLWHTZxvrxx9N5881JDB8+hp9/XsbQoQNp2rQllStXIS4uji5dHqNt247Mm7eYqKhTjBo19KbvOSNt48UXe7Nt2xbef/8jHnigFidPnuDSpYvXPSbrY6+/Tcfx40dZvvwnFi/+EYPBQOnSIQwfPoS1a9fSsmUrQFXiiYhYxQ8/hAN6Nm5cz4sv9ub99z+iceNmnDhxjFdeeRHQ8cYb4295fNT/ORNaK8yf1/R6k3MH0FnGrtebwInfS1Z6vYn4eBP336/ybYVjmUyqlfR//0FIiAk3N9f4OXNmRYuqutAnTqjrBoOpwNae5fZ1HBpAFy9enOK5WD0RFhbG1atX2blzp736xV9//YXVaqVhw4Y3fExoaCilSpXi0KFD2W4/fPgwHTp0yHabzWbjq6++omfPnrn64Hbv3m0P0PNDUpLjWlkmJOTuF3piYiKfffYZ8+fPt3+ec+bMISIigi+//JIRI0bY9x0/fjxt2qg8pgULFlCmTBl++uknunTpQmRkJE8++SQPPPAAaWlQunR5+2zM559/Qq1adZgw4R3M5jhMJj8++2weVaqEcOTIYSpVqgxAhQqVePvtqfbXK1++Al5e3vz66088+6yaNf7uu8V07Pgovr6+pKamMm3au/z66580bBgGQLly5dm8eQPz5n1Os2bNmTv3M8qVq8DkyR8AULlyFfbt28v06bc+Y5LVDz8sxWq1MmvWXPui1Nmzv6J0aX/Wr19Dq1ZtadHioWyP+fjjLyhd2p8NG9bSocMj9tu7dOlGjx7Zc/OtViuffz7fHmg/+2wP1q5dDbzDzbRt25EXX3wZgGHDRvHJJzNYt+5vKleuwnffLQZ0fPLJHDw8PKhWrTpnz55h0KB+N32+o0eP8uOP3/PrrxG0bNkaUJ/lnUpLS+OLLxZm+33Qtm0Hvv9+sT2A/vnnZRQrFkjz5i0BmDx5IsOGvU737r3srzt27CTefHPkbQJoIRwrY/GgNOnQjoxjERDg2HGITKVLq1Saa8ttNMcpcqCrVatG+/bt6devH7Nnz8ZsNjNo0CCeeeYZSl1bKnvmzBlatWrFwoULadCgATqdjhEjRjB+/Hhq1apF7dq1WbBgAQcPHmTZsmXZnv+vv/7ixIkT9O3bN8drL1iwADc3N+rUqQPAjz/+yLx585g7d27+v3ENO3bsGGazmSZNmthvM5lMNGjQIEfaTVhYmP1yQEAAVapUse8zePBgBgwYwB9//EGrVq1p3/5J7r+/JgYD7N27h3Xr/iYoKGe6xIkTx+wBdJ062UsKGo1GnniiC0uXLuLZZ3uQmJjI778vZ/78JdfGfpSkpCQefbRNtselpaVRq5Y6zgcPHuDBB7N/OWvQIIw7sXfvHo4fP0pwsG+221NSUjh+/BitWkFMTAyTJr3J+vVruHDhPBaLhaSkJKKisp8lqVs3e1lFgLJlQ7PNUgcHl+TChfM59svq/vtr2i/rdDqCgoLtjzly5BD3318zWypR/foNbvMe92IwGGjatPkt97udsmXvy/FlukuX7rzySj9mzPgUd3d3li5dxFNPPWM/s7F37x62bNnI++9nfmGwWCykpKSQlJRUKJsTCecgnQe1J+PXnrTt1g4Pj2TefrsTaWkXWbpUWnnftUWLFjFo0CBatWqFXq/nySef5KOPPrLfbzabOXToULbk76FDh5KSksKrr77K5cuXqVWrFhEREVSoUCHbc3/55Zc0btyYqlWr3vC1J02axKlTpzAajVStWpWlS5fy1FNP3XDfvODlpWaCHaGgY46+ffvSrl07fv/9d/744w+mTJnMpEkfMGjQKyQmJtChQycmTZpMenoCRqMPGaf9g4MzzwB4eeWcMu/atTvt2zfn/Pnz/P13BB4enrRp0x6AxET14S5b9julSpXO9rgbpd3crYSEBOrUqceXXy7KcV9goAoWX3qpF5cvX2Lq1JmEhNyHu7s7Dz0UhtmcfUHpjd7j9WdLdDrdbSu53M1jbuV2eft6vd5erSPDjVY43+j9dezYiUGDbKxc+Tv16j3Ipk3ree+9Gfb7ExMTGDNmIo8++sQdj0sIR7JYpPOgELdnZfv21eqSBquUOU0AHRAQwOLFi296f2hoaI4/1KDydF9//fVbPvetnrdXr1706tUr9wPNAzqd9vPiKlSogJubGxs3buS+++4DVGC0fft2hg4dmm3fLVu2ULZsWQCuXLnC4cOHqVatmv3+kJAQ+vfvT//+/Rk1ajQLF85h4MBXqFWrLsuX/8B994VisyVdW5iXu2oejRo1pkyZEH74YSkRESt4/PGn7cFj1arVcXd35/TpSJo1u/HMadWq1fj991+y3bZ9+511n6xduy4//riU4sVLZFs4mtWWLRuZMeNT+6LJ06ejruUPF7xKlaqwZMk3pKam2r9I7Ny5/ZaPqVGjBlarlQ0b1tpTOLIKDCxOfHw8iYmJeF/7of733925Go+HhwePPvoES5cu4vjxo1SqVCXb4sratety5MghKlSomMt3KIRjZfyJ8vWVzoNC3I67uzvz5s1n7949mmzl7TQBtNAWb29vBgwYwIgRIwgICKBs2bJMnTqVpKQkXnjhhWz7vvXWWxQrVoygoCDGjBlDYGAgnTt3BtRZgg4dOlC5cmWuXLnC2rV/U7VqNdLT4aWXBjJ//hx69+7GK6+8TPHiIRw/fpxly5Ywa9ZcDLc51/b009348svZHD16mPDwv+23+/r6MnjwcEaNehWr1UpYWFPi4mLZvHkjfn5+dO/eixde6M9HH33AmDEj6NWrL7t37+Sbb+bf0WfUtWt3Zs58n65dH+PNN9+idOkyREae4pdffuTVV0dSunQZKlSoxLfffk2dOvWJj49jzJgReDqo/VWXLt14660xvPLKiwwb9jqnT0cyc+Y04Oat5suWLUv37j0ZMKCPfRFhZOQpLlw4z5NPdqF+/YZ4eXkxYcIbDBgwmB07trJo0fxcj6lr1+489dQjHDiwj2eeeS7bfa+/Po6nnnqEMmXK0rnzU+j1evbu3cP+/f8xfvzbd/05iMLFalWbzZb5b9bLWe+//vbrH3OjXM2M/zo2W+bl0qWl8oYQt2M0GnnuuW6Eh/sTFKS9cFV7IxJOY8qUKVitVnr06EF8fDz169dn1apVFC1aNMd+Q4YM4ciRI9SuXZtff/3VXufXYrEwcOBATp8+jZ+fH+3bt+e992Zgs0HJkqX488+NjB07kieeeIK0tDRCQu6jTZv2uaor3bVrd95//x3Klr2PsLAm2e4bN24SgYHFmTZtMidPHqdIEX9q167L8OFvABASUpZFi37g9ddfZfbsj6lXrwETJrzLgAF9cv35eHl5sWrVOsaOHUW3bk+QkBBPqVKlad68Fb6+akb600+/5JVXXqRp07qUKRPC+PHvMmbM8Fy/Rl7y8/Pju+9+ZejQATRuXJsaNR7g9dfH0adPN9zdb54S8eGHnzJhwpu8+urLXL58iZCQsvbPMSAggLlzv+HNN0cwf/4cWrRoxRtvTLhWLeP2mjd/iKJFAzhy5BBdunTLdl/r1u1Ytuw3pkx5ixkz3sNkMlG5clV69cq5lkE4p6xnbRMTVepDRkCbcf+tgt+sAW3WQDbrbTqdauZ0/WW9Xm2QedlgUM2eMv7NelvG9Ywt63NkbFarKs117YScECKX8jC7Ms/obDfKexB5Li4ujiJFihAbG5ujDvSJEycoV66c5G1eY7Vm5oCrSWarvQqHk3SfdxlLly6if//nOXs29gYz4855XFJTU4iKOkF6ejlcuQ702bPhlCrVUXNl7KxWVXc3PV0FxFn/TU/PHuAaUxN55BlVkmj18gRsXt7ZAtMbBbLXB7Q3CmTv9vZ7ZTabCQ8Pp2PHjgVWkkvkjhwb7bFYLGzbto2NGzcyaNCgAouRbhavXU9moIXm6PXq9GZysqyILmiLFy8kNLQ8pUqVZu/ePYwdO4onnujisLQS4RyuD4SzBsQWS2b6gs2Wc9bWZFI5wR4eqqW1m1vm7cbUzNdo2hT0vtmDXCGE60pJSaFx48aAKjigtUlGCaCFJhmNmX9wZaV6wYmJiebtt8cRExNNcHBJHn/8acaPv3ldaeG6bhUQW1QTSftssV6fGRAbDCoI9vVVAbGnpwqGTabMwDjr5Vs0jYTMRoTqFK5MDApRaOh0Ou677z6SkpJuug7HkSSAFppkMKg/rmaz+lcUjFdfHcmrr4509DBEPrDZcgbC1/+b1fUpER4embPEHh63DohvGRQLIUQueHl5ceTIEcLDwzVZ119+zQlN0unUH+O0NO12IRJCi6xWiItTl8+cUf9mnM25PkfYy0sFxO7umTPFWQPhrMGxpFMJIUQmCaCFZmX80dZg/fQ7kvEF4Pp/b+Vuzlbd6xkuDZ4hE7lks6kqFbGx6v+Lj1p3R40aKji+2Wyx5BELIcTdkQBaaJZer/7Ip6RoJ7jLGgTfKDC+PjjOGHfWf7O+l5sF07cKsu92Rv5eZ/JvVuM2LS1zYZhWjlNhkZSkZpvT0lTzpbJlIThY5R//+SeEhkoKlBDCOaWkpNClSxdiYmJ46KGHNFcdRQJooWkmE6Sm3n6/u3GzYPhmM8U3C4azlrm6fsu63/XXcxPQ3knQe6cB8r0+t82mAjhPz+wlyLKW/hJ5LyVFzTSnpqoUjOBgtQUEqGMBau2AEEI4M4vFwq+//mq/rDUSQAtNy8jXTE+/+T63mgnOTTCc9fL1tWCz7nOjgPheZlxz81gtz+hmpNa4uak0gYzqDGZz5sI0nU4dQwmm701ampppTkxUC/gCAqBUKfVvRrqGEEK4Ejc3Nz777DP27t1rb76mJRJAC03LWEyYnp45q3ajYPj62eHrA+HczAhrOVjVOp0uc2Gam5sKrjOC6YzZaQmm70x6ugqaExLU/wF/f6hcWQXNfn7y8yqEcG0mk4kXXniB8PBwzaVvgDO1DyuE0tLUKfKC2tLSHP2ObyyjJFatWuWZM+dDvLxUvmdAgI7Vq3/Gx0fNwvn6Zv7r66v28fJSM3bu7iqwc3PLXlUgI6C7k2BkwoQJ1K5d+6b3r1mzBp1Ox9WrV+/pfecVnU7Hzz//XICvl1kL2NtbHRNvb3XdZlM/Zxmz1CI7iwWuXoXTpyE6Wn1mDzwATZpA48ZQrhwUKSLBsxBCOJrMQGtUWhps25bZ0rog+PhAgwbqj7aWZJ0pNpmuNVQAzp07R9GiRXNVc3bChAn8/PPP7N69O9/GqVUZn5OjZHxRyToznXFGIeOsQl62S3Y2Ga3rM0rP+fpClSoQGKhmnaWmshCiMLJarezbt4/IyEisGizHJb+aNSo9Xf1RzcgvzW+pqer10tPzN4BOS0vLs1ym4ODgPHkeZ2Sz2bBYLBhzEV1p6XPKCJQzvghlDaazLkI0GMBszrufFa3JKDsXF6c+Ax8fKF8eSpRQKRoaPFsphBAFKjk5mTp16gDQo0cP3AsiGLoDksKhce7umR3A8nO7m5/LFi1aMGjQIAYNGkSRIkUIDAxk7Nix2LIkKYeGhjJp0iR69uyJn58fL774IgAbNmygWbNmeHp6EhISwuDBg0lMzOzbe/78eTp16oSnpycVKlTgu+++y/H616cmnD59mmeffZaAgAC8vb2pX78+W7duZf78+UycOJE9e/ag0+nQ6XTMnz8fgKtXr9K3b1+KFy+On58fDz30EHv27Mn2OlOmTCEoKAhfX19eeOEFUlJS7vizut37/frrr6lfvz6+vr4EBwfTrVs3zp8/b78/Iy1kxYoV1KtXD3d3dzZs2ECLFi0YPHgwI0eOJCAggODgYCZMmHDTz+nkyZPodDp+/PFHWrZsiZeXF7Vq1WLz5s3ZHjNnzhxCQkLw8vLi8ccfZ/r06fj7+9/yPd7s8wfo3bs3nTt3zrb/sGFDadOmBZ6eatb1scdaMHr0IEaPHsp99wXy6KPt6N27Gz17ds32OLPZTNmygSxevBBQsxTTpk2mRo1yBAZ60qhRLX76adntDolDJCer1IzISFVNIyQEGjaEpk1VqkZQkATPQgiRITAwED8/P0cP44YkgBb3ZMGCBRiNRrZt28bMmTOZPn06c+fOzbbPtGnTqFWrFv/88w9jx47l2LFjtG/fnieffJJ///2XpUuXsmHDBgYNGmR/TO/evYmKiuLvv//mu+++48svv8wWUF4vISGB5s2bc+bMGX755Rf27NnDyJEjsVqtdO3alddee40aNWpw7tw5zp07R9euKih7+umnOX/+PCtWrGDnzp3UrVuXVq1acfnyZQC+++47JkyYwLvvvsuOHTsoWbIkn3766R19Rrl5v2azmUmTJrFnzx5+/vlnTp48Se/evXM81+uvv86UKVM4cOAANWvWtB8Db29vtm7dytSpU3nrrbeIiIi45ZjGjBnD8OHD2b17N5UrV+bZZ58l/Vqpk40bN9K/f3+GDBnC7t27adOmDe+8884tn+9Wn39uZCziXLRoAd7ebqxbt5FPP51N167dWbHiVy5fTiA9XaU7/PnnKpKTk+jU6XEApk2bzOLFC5k5czbbt+9j0KBX6dv3OdavX5ur185vKSlw/rwKmhMS1CxzgwbQrBnUrg0lS6ovsUIIITJ5e3tz9uxZFi5ciLe3t6OHk4OkcIh7EhISwowZM9DpdFSpUoW9e/cyY8YM+vXrZ9/noYce4rXXXrNf79u3L927d2fo0KEAVKpUiY8++ojmzZvz2WefERkZyYoVK9i2bRsPPvggVquVjz/+mIYNG950HIsXL+bChQts376dgIAAACpWrGi/38fHB6PRmC2dYcOGDWzbto3z58/bTw1NmzaNn3/+mWXLlvHiiy/y4Ycf8sILL/DCCy8A8Pbbb/Pnn3/e0Sz05MmTb/l+PTw86NOnj33/8uXL89FHH/Hggw+SkJCAT5Y6ZW+99RZt2rTJ9vw1a9Zk/Pjx9uf+5JNPWL16dY79sho+fDgPP/wwABMnTqRGjRocPXqUqlWr8vHHH9OhQweGDx8OQOXKldm0aRO//fbbTZ/vdp9/blWqVIn3359qv161agW8vb2JiPiJLl16YLHAkiWLad/+Uby9fUlNTWXatHf59dc/adgwDIBy5cqzefMG5s37nGbNmt/xGPKC2axqNSclqZSo68vOFcZcbyGEcCUyAy3uSaNGjdBliQbCwsI4cuRItqLn9evXz/aYPXv2MH/+fHx8fOxbu3btsFqtnDhxggMHDmA0GqlXr579MZUrV75lCsHu3bupU6eOPXjLjT179pCQkECxYsWyjeXEiRMcO3YMgAMHDuQI3MPCwnL9Grl5vwA7d+6kU6dOlC1bFl9fX5o3V4FfZGRktue6/rME7DPRGUqWLHnL2frrH1OyZEkA+2MOHTpEgwYNsu1//fXr3c3nfyNZjzmAyWSkS5cufPfdIry9QadLZMWK5Tz7bHesVjh48ChJSUk8+mgbgoJ87NvixQs5ceLYPY3lTqWnw5UrEBUFFy6oCjA1a6oKGo0awX33qVQVCZ6FEML5yQy0yHfXn3pJSEjgpZdeYvDgwTn2LVu2LIcPH77j1/DMaMF2BxISEihZsiRr1qzJcd/t8n3v9HVu9X4TExNp164d7dq1Y9GiRRQvXpzIyEjatWtH2nW1BW90Guv6+pg6ne62qRNZH5PxBeheVjnf7vPX6/XZcuNBpa1c70bvr3v37jRv3pwLF84TERGBp6cnnTu3R68Hq1WVqfnuu98JCiptTwUxGCiQBScWi0rLiI9Xr+vnB9WrqwoaRYqocQghhLhzKSkpPP/885w9e1ZaeQvXk7FILMOWLVuoVKkShltEDnXr1mX//v03PcVftWpV0tPT2blzJw8++CAAR44cuWVd5Zo1azJ37lwuX758w1lQNze3HK1A69atS3R0NEajkdDQ0Bs+b7Vq1di6dSs9e/bM9h7vxO3e7969e7l06RJTpkwhJCQEgB07dtzRa+SlKlWqsH379my3XX/9erf7/IsXL85///2X7bbdu3fn6hdi48aNCQkJYenSpaxYsYKnn37a/rhatarj7u7OhQuRtGnT3F7NI+O7gMWS9+XxbLbMsnM2m0rJqFQJiheHokWl7JwQQuQFi8XCkiVL7Je1RlI4xD2JjIxk2LBhHDp0iG+//ZaPP/6YIUOG3PIxo0aNYtOmTQwaNIjdu3dz5MgRli9fbl9UV6VKFdq3b89LL73E1q1b2blzJ4MHD77lLOezzz5LcHAwnTt3ZuPGjRw/fpwffvjBXl0iNDSUEydOsHv3bi5evEhqaiqtW7cmLCyMzp0788cff3Dy5Ek2bdrEmDFj7AHskCFDmDdvHl999RWHDx9m/Pjx7Nu3744+o9u937Jly+Lm5sbHH3/M8ePH+eWXX5g0adIdvUZeeuWVVwgPD2f69OkcOXKEzz//nBUrVmRL1bne7T7/hx56iB07drBw4UKOHDnC+PHjcwTUt9KtWzdmz55NREQE3bt3t9/u6+vL8OHDee21V1m8eAHnzh3j8OFdzJ//Md9/vwBQAXVaWmaZvLuRUXbu3DmVomE2q5SMhg3VYsDq1VUALcGzEELkDTc3N6ZNm0afPn00WdJUAmiNS01Vq/jze0tNvbvx9ezZk+TkZBo0aMDAgQMZMmSIvVTdzdSsWZO1a9dy+PBhmjVrRp06dRg3bhylSpWy7/PVV19RqlQpmjdvzlNPPUWvXr0oUaLETZ/Tzc2NP/74gxIlStCxY0ceeOABpkyZYp8Jf/LJJ2nfvj0tW7akePHifPvtt+h0OsLDw/nf//7H888/T+XKlXnmmWc4deoUQUFBAHTt2pWxY8cycuRI6tWrx6lTpxgwYMAdfUa3e7/Fixdn/vz5fP/991SvXp0pU6Ywbdq0O3qNvNSkSRNmz57N9OnTqVWrFitXruTVV1/F4xalIm73+bdr187+OT744IPEx8dnm9W/ne7du7N//35Kly5NkyZNst03adIkxo4dy+TJk6lWrRodO7Zn5crfqVKlnL0jpaenmonOqDltseTuZz4lBWJiVAWN5GS1EDCj7FytWhAcrL3GQ0II4QpMJhODBw/m0Ucf1Vz6BoDOdn1iosgXcXFxFClShNjY2Gw1DVNSUjhx4gTlypXLFqA4QyfCFi1aULt2bT788MN8HZfVaiUuLg4/Pz/0evnO5wj9+vXj4MGDrF+/3n6bsx0Xm00FzklJKRw/foLo6HIkJXnY61BnnOBITVXpGUlJ6rasFTQ0WEnphsxmM+Hh4XTs2FGTf3hyLTFR/WIC9cvQWQ7ATbjMcXFBcmy0yRHH5Wbx2vXkhKNGubmpYPZaad4CYTTKbJpQpk2bRps2bfD29mbFihUsWLDgjutfa41Ol/kz7uYG9eqpIPncObh6VdVq1unUfUWLQtWqKmiWyhlCCFHwrFYrJ0+eJCYmRlp5izuT8YdeiIK2bds2pk6dSnx8vL0udd++fR09rDyj06nAuHhxKFtWTW5evaq+sPr7qwoaTjCpLoQQLis5OZnKlSsD0KVLF8218pYAWty1G5V/E67hRq3TXVVGMO3r6+iRCCGEyMrLy0uTFThAAmghhBBCCKEx3t7eXL16lfDwcE228naak5SXL1+me/fu+Pn54e/vzwsvvEDCLVbYnTx5Ep1Od8Pt+++/t+8XGRnJww8/jJeXFyVKlGDEiBGkX5d4vGbNGurWrYu7uzsVK1Zk/vz5ef7+ZC2nEAVD/q8JIYS4V04TQHfv3p19+/YRERHBb7/9xrp1625ZLi0kJIRz585l2yZOnIiPjw8dOnQAVGHuhx9+mLS0NDZt2sSCBQuYP38+48aNsz/PiRMnePjhh2nZsiW7d+9m6NCh9O3bl1WrVuXJ+8pYVZqUlJQnzyeEuLWM/2uy0l4IIcTdcooUjgMHDrBy5Uq2b99O/fr1Afj444/p2LEj06ZNy1Y/OIPBYCA4ODjbbT/99BNdunTB51pZpD/++IP9+/fz559/EhQURO3atZk0aRKjRo1iwoQJuLm5MXv2bMqVK8cHH3wAqM50GzZsYMaMGbRr1+6e35vBYMDf35/z588DKt/nVg0rCiOr1UpaWhopKSlOUS6tsHC242Kz2UhKSuL8+fP4+/vfslumEEIIx0pNTeXll18mKiqKVq1aaW7SwykC6M2bN+Pv728PngFat26NXq9n69atPP7447d9jp07d7J7925mzZqV7XkfeOABe9MMUA0fBgwYwL59+6hTpw6bN2+mdevW2Z6rXbt2DB069Javl5qaSmqWTg1xcXGAqmloNpuz7VusWDEsFgsxMTG3fR+Fkc1mIyUlBQ8PD/lyoSHOelz8/PwoVqxYjv+HriTjvTn9ezSbMdkvmlUXHCfmMsfFBcmx0Z7k5GTmzZtnv1xQVThy+zPgFAF0dHR0ji50RqORgIAAoqOjc/UcX375JdWqVaNx48bZnjdr8AzYr2c87832iYuLIzk5+abtpSdPnszEiRNz3P7HH3/g5eV1w8fodDqZFRMiH1kslkKVAx0REeHoIdwTQ0oKj1y7vGrVKiy36IbpTJz9uLgyOTbaYTab6d69OwBr164tsBno3KbUOjSAfv3113nvvfduuc+BAwfu+XWSk5NZvHgxY8eOvefnyq3Ro0czbNgw+/W4uDhCQkJo27btLTvbiJzMZjMRERG0adNGc6dwCjM5LtrlMscmMdF+sV27di7RidAljosLkmOjTR07dizw45KRMXA7Dg2gX3vtNXr37n3LfcqXL09wcLA9RzhDeno6ly9fzpHnfCPLli0jKSmJnj17Zrs9ODiYbdu2ZbstI40i43mDg4NzpFbExMTg5+d309lnAHd39xuebjCZTPKf8y7JZ6dNcly0y+mPTZaxm0ymbNedmdMfFxcmx0abCvK45PZ1HBpAFy9enOLFi992v7CwMK5evcrOnTupV68eAH/99RdWq5WGDRve9vFffvkljz76aI7XCgsL45133uH8+fP2FJGIiAj8/PyoXr26fZ/w8PBsj4uIiCAsLCxX71EIIYQQQtwZm83GhQsXiI2N1WTqnfaXzqMqX7Rv355+/fqxbds2Nm7cyKBBg3jmmWfsFTjOnDlD1apVc8woHz16lHXr1t2wDXHbtm2pXr06PXr0YM+ePaxatYo333yTgQMH2meP+/fvz/Hjxxk5ciQHDx7k008/5bvvvuPVV1/N/zcuhBBCCFEIJSUlUbp0aXr16qXJUr9OsYgQYNGiRQwaNIhWrVqh1+t58skn+eijj+z3m81mDh06lONDnjdvHmXKlKFt27Y5ntNgMPDbb78xYMAAwsLC8Pb2plevXrz11lv2fcqVK8fvv//Oq6++ysyZMylTpgxz58694xJ2Gd+ecptbIzKZzWaSkpKIi4uTU2saIsdFu1zm2GTJgSYuDjTa0je3XOa4uCA5NtqTmOX/f1xcXIGVS82I0243662zaXFe3AWdPn2akJAQRw9DCCGEEELcRlRUFGXKlLnp/RJAFxCr1crZs2fx9fV1qpq5WpBRwSQqKkoqmGiIHBftkmOjTXJctEuOjTY54rjYbDbi4+MpVarULWe9nSaFw9np9fpbfpMRt+fn5ye/2DRIjot2ybHRJjku2iXHRpsK+rgUKVLktvs4xSJCIYQQQgghtEICaCGEEEIIIe6ABNBC89zd3Rk/fvwNG9MIx5Hjol1ybLRJjot2ybHRJi0fF1lEKIQQQgghxB2QGWghhBBCCCHugATQQgghhBBC3AEJoIUQQgghhLgDEkALIYQQQghxBwpdAL1u3To6depEqVKl0Ol0/Pzzz7fc/8cff6RNmzYUL14cPz8/wsLCWLVqVcEMVgghhBBCaE6hC6ATExOpVasWs2bNytX+69ato02bNoSHh7Nz505atmxJp06d+Oeff/J5pEIIIYQQQosKdRk7nU7HTz/9ROfOne/ocTVq1KBr166MGzcufwYmhBBCCCE0y+joATgbq9VKfHw8AQEBt9wvNTWV1NTUbI+7fPkyxYoVQ6fT5fcwhRBCCCHEHbLZbMTHx1OqVCn0+psnakgAfYemTZtGQkICXbp0ueV+kydPZuLEiQU0KiGEEEIIkVeioqIoU6bMTe+XFI47SOFYvHgx/fr1Y/ny5bRu3fqW+14/Ax0bG0vZsmU5ceIEvr6+9zLsQsccF8ffW7bQ0t8fk4eHo4cjrjHbbPwdE0PLoCBMclZFU1zm2KSkYOrYEQBzeDg4+f//Aj0uFgtcuKA+s0qVIDgYnPlnIZ+ZzWb+/vtvWrZsiclkcvRwBGrN2n333QfAsWPHKFKkSIG8bnx8POXKlePq1au3fE2Zgc6lJUuW0LdvX77//vvbBs+g+rffqHd7QEAAfn5++TFEl2U2GPDy8qKYry8mT09HD0dcY7Za8YqPV8flFqe5RMFzmWOTNZDx9QUn//9f4MelaFG4dAmOH1fBc6VK4OaW/6/rhMxms/o7U6yYBNAa4enpyX333UdSUhLFihUrsAA64/jfLt1WAuhc+Pbbb+nTpw9Llizh4YcfdvRwhBBCiNwpVgySk+HQIYiPh2rVoIACESHuhZeXF0eOHCE8PBwvLy9HDyeHQhdAJyQkcPToUfv1EydOsHv3bgICAihbtiyjR4/mzJkzLFy4EFBpG7169WLmzJk0bNiQ6OhoQH0zKqhvQ0IIIcRd8/SE0qUhOjoziC5dWlI6hLgHTnxu7+7s2LGDOnXqUKdOHQCGDRtGnTp17CXpzp07R2RkpH3/L774gvT0dAYOHEjJkiXt25AhQxwyfiGEEOKOGY1QpgzYbLBrF+zfD2lpjh6VEE6r0M1At2jRglutm5w/f36262vWrMnfAWVhsVgwm80F9nrOwpyWhtFoJEWnw+LoweQDE2Bw9CCEEIWDpHQIJ5GSkkKXLl2IiYnhoYce0lxueqELoLXIZrMRHR3N1atXHT0UTbJZrQQHBxOl16Nz5gVRN2Kzgc2Gv9VKMCAnVIUQ+S5rSkdCggqiS5WSlA6hKRaLhV9//dV+WWskgNaAjOC5RIkSeHl5SaOV61jT00lISsLHYEDvYp+NzWYjKTWV8xcvgtlMSUcPSAhROGSkdFy6BDt3QmwsVKwoVTqEZri5ufHZZ5+xd+9e3DT4cykBtINZLBZ78FysWDFHD0eTrOnppKWn42Ew3LIrkLPyvFbb9nxMDCWsVknnEEIUnGLFICkJDh6EuDhJ6RCaYTKZeOGFFwgPD9dc+gYUwkWEWpOR86zFEi2i4Hi5u4NOh2TACyEKnJeXSumIiYHt2+HMGZVeJoS4KQmgNULSNgo3nU4n+YdCCMfJSOmwWlVKx4EDUqVDOJTVamXfvn1ERkZitVodPZwcJIVDCCGEEIqkdAiNSE5Otpcc7tGjxw27OzuSzECLfLNmzRp0Op1UFxFCCGciKR1CIwIDA/Hz83P0MG5IZqCFEEIIkZ1U6RAO5u3tzdmzZwkPD8fb29vRw8lBZqCFyGNpkjcohHAVxYpB0aIqpWPXLhVICyEkgBZ3LzU1lcGDB1OiRAk8PDxo2rQp27dvz7Hfxo0bqVmzJh4eHjRq1Ij//vvPft+pU6fo1KkTRYsWxdvbmxo1ahAeHn7T1wytXp1JU6bwbO/eeJcoQelKlZj1+efZ9omMiuKxrl3xCQrCr2RJuvToQUxMDACxsbEY/PzYsWsXoBYpBISE0KhlS/vjv1myhJAqVezXo06fpkuPHviXLk1ASAiPde3KyVOn7Pf3fuklOj/zDO9MnUqpihWpci1nSwghXIKkdAiRgwTQWmSzQWKiY7Y7+KU4cuRIfvjhBxYsWMCuXbuoWLEi7dq14/Lly9n2GzFiBB988AHbt2+nePHidOrUyV6+b+DAgaSmprJu3Tr27t3Le++9h4+Pzy1f9/2ZM6n1wAP8s3Ejrw8bxpCRI4n46y9ABcSPde3K5cuXWbtyJRG//MLxkyfp2qsXAEWKFKF2zZqsWb8egL379qHT6fhnzx4SEhIAWLthA82bNgVUmcF2jz2Gr68v61etYmNEBD7e3rTv3DnbTPPqNWs4dOQIEb/8wm/ff5/rz1AIIZxCRkqHxSJVOkSBSElJoWfPnkyfPp2UlBRHDycHyYHWoqQkuE0QmW8SEiAXuUaJiYl89tlnzJ8/nw4dOgAwZ84cIiIi+PLLLxkxYoR93/Hjx9OmTRsAFixYQJkyZfjpp5/o0qULkZGRPPnkkzzwwAMAlC9f/rav3aRRI15/7TUAKleqxMYtW5jxySe0eeghVq9Zw959+zixbx8hZcoAsPCLL6jx4INs37mTB+vVo0WzZqxZv57hQ4awZt062rRsycHDh9mweTPt27Rhzfr1jBw6FIClP/yA1Wpl7qxZ9lKDX82ejX/p0qxZv562rVoB4O3lxdxZszTZLUkIIfJMYKD6G3XggKrSUb06aHSRl3BuFouFJUuW2C9rjcxAi7ty7NgxzGYzTZo0sd9mMplo0KABBw4cyLZvWFiY/XJAQABVqlSx7zN48GDefvttmjRpwvjx4/n3339v+9phDRrkuH7g0CEADhw8SEiZMvbgGaB6tWr4+/vb92netCkbNm/GYrGwdsMGWjRrZg+qz547x9Fjx2jRrBkAe/bu5ejx4/gGB+MTFIRPUBABISGkpKRw7Phx+2s8UKOGBM9CiMLBy0vNRmekdERHO3pEwgW5ubkxbdo0+vTpo8m/rzIDrUVeXmom2FGvXYD69u1Lu3bt+P333/njjz+YPHkyH3zwAa+88kq+veb/mjQhPj6eXbt3s27jRt6dMIHgoCCmTJ9Orfvvp1TJklSqWBGAhIQE6tWpw6Ivv8zxPMUDA+2XtbhCWAgh8k1GSseFCyqlo2pVKFcO9DIvJ/KGyWRi8ODB0spb3AGdTqVROGLLZTe8ChUq4ObmxsaNG+23mc1mtm/fTvXq1bPtu2XLFvvlK1eucPjwYapVq2a/LSQkhP79+/Pjjz/y2muvMWfOnFu+9pbrFipu2b6datcW/VWrWpWo06eJOn3afv/+Awe4evUq1atWBcDf35+a99/PJ59/jslkomqVKvyvSRP+2bOH31autOc/A9StXZsjx45RonhxKlaokG0rIs0FhBCFXfHi6m/Hv//Cf/9BaqqjRyREgZAAWtwVb29vBgwYwIgRI1i5ciX79++nX79+JCUl8cILL2Tb96233mL16tX8999/9O7dm8DAQDp37gzA0KFDWbVqFSdOnGDXrl38/fff2YLrG9m4ZQtTZ8zg8JEjzPr8c77/6SeGvPwyAK1btuSBGjXo3qcPu3bvZtuOHfR88UWaN21K/bp17c/RolkzFi1dag+WAwICqFalCkt/+CFbAN29a1cCixXjsa5dWb9xIydOnmTNunUMHj6c02fO5MVHKYQQzs3XF0qUgKNHVam7uDhHj0i4AKvVysmTJ4mJidFkK28JoMVdmzJlCk8++SQ9evSgbt26HD16lFWrVlG0aNEc+w0ZMoR69eoRHR3Nr7/+as9nslgsDBw4kGrVqtG+fXsqV67Mp59+esvXfe2VV9ixaxd1mjTh7alTmT55Mu1atwZAp9OxfOlSihYtyv/ataN1p06UDw1l6YIF2Z6jedOmWCwWe64zqKD6+tu8vLxYt2oVZUNCeKJbN6rVq8cLAweSkpKCn6/vPX1+QgjhMjw8VErH+fMqL/pa6VAh7lZycjKVK1fmpZdeIjk52dHDyUFns0kxx4IQFxdHkSJFiI2NzdaWMiUlhRMnTlCuXDk8PDwcOELtsqanE5eQgJ/BQPn772fowIEMHTjQ0cPKUympqZyIiqJcejrO8lNgtloJP3uWjqVKYZK8R01xmWOTnAwZX2jXrwdPT8eO5x65zHG5FZtN5UVbrVCtGoSGOkVetNlsJjw8nI4dO2oy37YwSkxMpESJElgsFqKjo/H39y+Q171ZvHY9WUQohBBCiLyh06l0jrg4lRedmAhVqkgLcHHHvL29uXr1qmZbeUsALYQQQoi85eengubDh1UQXaOGypUWwkVIAC2cysn9+x09BCGEELmRkRd97pxKx6lRQ81OC+ECtJ+YJIQQQgjnlFEvOjkZduyAkydVfrQQt5Gamkr//v2ZNWsWqRosjygz0EIIIYTIPzodBAVBbCzs2aMahVWuLHnR4pbS09OZN2+e/bLWSAAthBBCiPxXpAi4u2fPi/bxcfSohEaZTCYmTpzI4cOHNVkZRQJoIYQQQhQMDw8oXTp7XnTx4o4eldAgNzc3Ro8eTXh4uL13hJZIDrQQQgghCk5GXnRiIuzcCadOqfrRQjgRCaCFEEIIUbB0OggOBpMJdu+G/fvBbHb0qISG2Gw2Lly4QGxsLFrs+ScpHFqWlgYFmThvNGp6UYerdiEUQohCy99f5UUfOgRJSVC9OmiwaYYoeElJSZQuXRqARx99VHNpHBJAa1VaGmzbplYrFxQfH2jQQNNBtBBCCBfj6anyos+cUUF0jRoQGOjoUQlxSxJAa1V6ugqe3dzUt/P8lpqqXi89PV8D6LS0NM19iyxIFosFnU6HXi/ZU0IIYZeRFx0To/Kiq1WDkBCV6iEKJW9vb9LS0jTbylv+imudu7tatZzf210E6S1atGDQoEEMGjSIIkWKEBgYyNixY7PlKoWGhjJp0iR69uyJn58fL774IgAbNmygWbNmeHp6EhISwuDBg0lMTLQ/7vz583Tq1AlPT08qVKrEd999d9vx9H7pJTo/8wwT332X4vfdh1/JkvQfPJi0tDT7PqmpqQwePpwSoaF4FCtG0zZt2L5zp/3++s2aMW3mTPv1zs88g8nfn4RrZwJOnzmDzseHo8eO2Z9v+BtvULpSJbxLlKBhixasWbfO/vj533yDf+nS/PL771SvVw/3gAAio6Lu9KMWQgjXl5EXbTTCP//AgQOSFy00SwJocU8WLFiA0Whk27ZtzJw5k+nTpzN37txs+0ybNo1atWrxzz//MHbsWI4dO0b79u158skn+ffff1m6dCkbNmxg0KBB9sf07t2bqKgo/v77b75bsoQvv/yS8xcu3HY8q9es4cChQ6xZsYJvv/qKH3/5hYnvvmu/f+Sbb/LD8uUs+OILdm3YQMXy5WnXuTOXL18GoHnTpqxZvx5QCxjWb9qEf5EibNi8GYC1GzZQulQpKlaoAMCg115j87ZtLJk/n3+3bOHpxx+n/eOPc+ToUftrJiUl8d6MGcydNYt927dTQko2CSHEzfn7Q0AAHDyoGq9kmVwRQiskgBb3JCQkhBkzZlClShW6d+/OK6+8wowZM7Lt89BDD/Haa69RoUIFKlSowOTJk+nevTtDhw6lUqVKNG7cmI8++oiFCxeSkpLC4cOHWbFiBXPmzKFRo0bUq1ePjz/+mOTk5NuOx83NjXmffUaN6tV5uH173nrzTT6aPRur1UpiYiKfzZ3L+++8Q4e2balerRpzPvkETw8Pvly4EIAWzZqxYfNmLBYL//73H24mE927drUH1WvWr6d506YAREZF8dXXX/P911/TrEkTKpQvz/AhQ2gaFsZX33xjH5PZbObTGTNo3KgRVSpXxsvLK68+fiGEcE1eXiovOipKtQC/dMnRIxIFLDU1lddee425c+dKK2/heho1aoQuS45aWFgYH3zwARaLBYPBAED9+vWzPWbPnj38+++/LFq0yH6bzWbDarVy4sQJDh8+jNFopF69evb7K1eujL+//23HU+v++7MFqGENG5KQkEDU6dPExsVhNptp0qiR/X6TyUSDevU4cOgQAM0aNyY+Pp5/9uxh05YtNG/alBbNmjFl+nRAzUCPGDIEgL379mGxWKhcu3a2MaSmplIsIMB+3c3NjZr333/bsQshhMgiIy86OloF0dWrq+uSF10opKen8/HHH9sva40E0CLfXZ/8n5CQwEsvvcTgwYNz7Fu2bFkOHz5cUEPLwd/fn1oPPMCa9evZvHUrbR56iP81aULXXr04fOQIR44etc9AJyQkYDAY2Ll+vf3LQgafLO1pPT09s33JEEIIkUt6PZQqBVeuqLzoxESoWFEF18KlmUwmRo0axbFjx6SVt3A9W7duzXZ9y5YtVKpUKUdAmVXdunXZv38/FStWvOH9VatWJT09nZ07d/Lggw8CcOTIEa5evXrb8ez57z+Sk5Px9PRU49m2DR8fH0LKlCEwORk3Nzc2btnCfWXLAiq9YvuuXdlqSzdv2pS/161j244dvDN+PAEBAVSrUoV33n+fksHBVK5UCYA6tWphsVg4f+ECzZo0ue3YhBBC3KWiRVWFqP37VRBdrZpK8xAuy83NjUmTJkkrb+GaIiMjGTZsGIcOHeLbb7/l448/Zsi1FIebGTVqFJs2bWLQoEHs3r2bI0eOsHz5cvsiwipVqtC+fXteeukltm7dys6dOxk8eLA9KL6VtLQ0Xnj5ZfYfOED4qlWMf+cdBr30Enq9Hm9vbwb07cuIMWNYGRHB/gMH6DdoEEnJybzQs6f9OVo0a8aqP//EaDRStUoV+22Lli61zz4DVK5Uie5du9LzxRf5cflyTpw8ybYdO5g8bRq/r1x5Nx+nEEKIm/H2VrPRkZGq1N21xd9COILMQGtdQSXO3+Xr9OzZk+TkZBo0aIDBYGDIkCH2UnU3U7NmTdauXcuYMWNo1qwZNpuNChUq0LVrV/s+X331FX379qV58+YEBQUxevRopkyefNvxtGrRgkoVKvC/du1ITUvj2aeeYsIbb9jvn/LWW1itVnr07Ut8QgL169Zl1c8/U7RoUfs+zRo3xmq1ZguWWzRrxsxPP6VFs2bZXu+r2bN5+733eO2NNzhz9iyBxYrR6MEHeaR9+9uOVQghxB0ymXLmRZcuLXnRLshms5GYmEhKSoomW3nrbFoclQuKi4ujSJEixMbG4ufnZ789JSWFEydOUK5cOTw8PDIf4ASdCFu0aEHt2rX58MMP83VY1vR04hIS8DMYbtmApPdLL3E1NpaflyzJ1/Hkh5TUVE5ERVEuPR2P2++uCWarlfCzZ+lYqhQmaQyjKS5zbJKTIeNL6/r1qmOdE3OZ46IVly9DSgpUrgwVKtxTXrTZbCY8PJyOHTtqMt+2MEpMTLSvJ7py5UquCgnkhZvFa9crdDPQ69at4/3332fnzp2cO3eOn376ic6dO9/yMWvWrGHYsGHs27ePkJAQ3nzzTXr37p2/A3VzU8FsQa48NRqljbcQQgjnEBCg8qH371ctwKtWdfovWcJ5FLoAOjExkVq1atGnTx+eeOKJ2+5/4sQJHn74Yfr378+iRYtYvXo1ffv2pWTJkrRr1y5/B+vmJgGtEEIIcTPe3iqt48QJFUzXqKEWHAqn5+XlxZUrV1i1apUm+ycUugC6Q4cOdOjQIdf7z549m3LlyvHBBx8AUK1aNTZs2MCMGTPyP4DWuDVr1jh6CNnM//xzRw9BCCFEQXNzg5AQOHcuMy+6VCnJi3ZyOp0Ob29vPDw8NFkKttAF0Hdq8+bNtG7dOttt7dq1Y+jQobd8XGpqarbOOXFxcYDKszKbzfbbzWazvYmI1WrNu4G7kIw0fRvgqp+QFfU+zVYrNy8AqC3maz+vZvm51RyXOTZWKxnZqGarFZz8/bjMcdGqkiVVx8KdOyEuDsqVg1uUVM0q4+9y1r/PwvEccVxy+1oSQN9GdHQ0QUFB2W4LCgoiLi4uW73h602ePJmJEyfmuP2PP/7IdirCaDQSHBxMfHw8aWlpeTt4FxNvsYDF4uhh5ItUs5lki4V1589rsuPSrURERzt6COImnP3YGFJSeOTa5VXnzmHxcJYltrfm7MfFKRw+rLY7FBERkQ+DEXfDbDazdOlS++WCWtyZlJSUq/0kgM4no0ePZtiwYfbrcXFxhISE0LZt22yrOi0WC8ePH0ev199ytWdhZrNYiE9MxNdgQOeiK9cvJSXhqdfTqkQJp5qBjoiOpk1wsFQU0BiXOTbJyfaL7UqWdPoFYi5zXJxBWhqcPw/Fi6vFhUWK3HJ3s9lMREQEbdq0kSocGpGYmMjTTz8NwKefflqgVThyQwLo2wgODiYmJibbbTExMfj5+d2ysYe7uzvu7u45bjeZTNn+c5pMJooWLcrFixfR6/V4eXlpMtfHkazp6aSlpZFqMKB3sc/GZrORlJrKxYsXKWq14uGEf1RNer0EAxrl9Mcmy9hNen22687M6Y+LM/DwUPWhz52D3bsz86Jv4/q/0cJxPD09eeWVVzhx4gSenp4Fdlxy+zoSQN9GWFgY4eHh2W6LiIggLCwsz14jODgYgPPnz+fZc7oSm9VKckoKnnq9681A22xgs+FvtRLs6LEIIYQrMRhU05WLF2HXLlXq7g7yooVjubu788EHHxAeHn7DCUlHK3QBdEJCAkePHrVfP3HiBLt37yYgIICyZcsyevRozpw5w8KFCwHo378/n3zyCSNHjqRPnz789ddffPfdd/z+++95NiadTkfJkiUpUaKELGC4AXNcHOu2b+d//v6YXCQHMisTOE3ahhBCOJ3AQNWUbO9e9W/VqmqGWoh7UOgC6B07dtCyZUv79Yw85V69ejF//nzOnTtHZGSk/f5y5crx+++/8+qrrzJz5kzKlCnD3Llz86WEncFgwCDfjHMwpKSQnp6Oh82GnFgTQghxx3x8VL3o48dVbn316rfNixbiVgpdAN2iRYtb9lSfP3/+DR/zzz//5OOohBBCCJGv3N1VSse5cyqdo0YNCJbkOa1yVCvv3HKxhFIhhBBCiJvIyIs2m1W96GPHnL6+uHCMQjcDLYQQQohCrnhxiI+Hf/9VLcArVHD0iMR1vLy8OHPmDH/++ae08hZCCCGE0ARfX5UXffSoCqaFpuh0OooXL06RIkU0Wd5XUjiEEEIIUTh5eGSWugM4e1ZSOkSuSAAthBBCiMLLYICSJdXl3bsz0zqEQ6WlpTF58mS+//570tLSHD2cHCSAFkIIIUThlpEiUKyYKnW3dSucOaOaXQmHMJvNjB8/nkWLFmmyR4bkQAshhBBCgCp1V7YsXLoEO3aozoUVK4IGF7G5OqPRSJ8+fYiKisJo1F64qr0RCSGEEEI4ik6nuhempKgFhpcvQ+XKKs1Dg4vZXJW7uzuzZ8+WVt5CFHo2G0REwIEDalajcmUoXx7c3Bw9MiGEENfz8FCz0RcvZp+N9vR09MiEBkgALURBOHAA3n9fLU7JymBQQXSVKiqgrlxZXfb1dcw4hRBCZNLpVM3orLPRVapAUJDMRhdyEkALkZ+uXIFPP4Wff1Yz0J6e0KaNaiV76BDExcGRI2rLqlSpzGA641/5hS2EEI7h4QEhIXDhAmzbphqvVKigbhf5IjExkRIlSmCxWIiOjtZcK28JoIXID+npsGwZfP55ZoH+Dh3glVegRAl13WaDmBgVSB8+nPnv2bOZ25o1mc9ZpEjmLHVGUB0aChpcXCGEEC5Hp1O/v5OT1e/rixehalV1m0xu5IukpCRHD+Gm5C+vEHlt+3aVrnH8uLpepQqMGAG1a2ffT6eD4GC1NW+eeXt8fPaA+vBhOHYMYmPVc2/fnrmvm5uaBck6W12pEnh75/vbFEKIQsnTM3M2evt2lYZXoYKq4CHyjKenJ4cPH+bvv//GU4N55xJAC5FXzp6FDz+Ev/5S14sUgYED4bHHVK5zbvn6Qr16asuQlqYC8oyAOiO4TkxU+dUHDmR/jpCQ7HnVVauq+qYySyKEEPdOr1dpdUlJcPCgKntXpUrmGUZxz/R6PaGhoQQFBaHXa69tiQTQQtyrlBRYsAAWLoTUVBUsP/UUvPQS+PnlzWu4uakguGrVzNusVhW0Xz9bHRMDUVFq+/PPzP0DArKnf1SpogLtOwnuhRBCZPLyUr9Hz59Xs9EVKqhqHTIb7fIkgBbibtlsarZ5xgyIjla31a8Pw4erUkf5Ta+HMmXU9tBDmbdfuZJzpvrkSbV6fMsWtWXw8FApH1kD64oVZWGMEELkll6vUvESE2H/fpUbXaWKqt4h7prZbOajjz5i//79tGnTBpPJ5OghZSMBtBB34+hR+OCDzHzk4GAYOhRatXJ8mkTRotCwodoypKSoPOqsM9WHD6vb9+5VWwa9Hu67L2cVkKJFC/69CCGEs/D2VpMP58+rSh0VK6rZaKn1f1fS0tIYPnw4AB988AFeGusGKQG0EHciLg6++AK+/x4sFnWarmdP6NVL27O2Hh5Qo4baMlgsKs0j62z1oUNqpvrECbWtWpW5f/Hi2fOqK1cu+PchhBBaZjCojoUJCfDff+r3aaVKqrOhuCMGg4FnnnmGs2fPYtBgqqEE0ELkhsUCv/wCs2bB1avqtoceUrPOpUo5cmR3z2BQZfBCQ6Ft28zbL17Mnv5x6JAKtC9cUNuGDQCYgI5eXhgaNoRmzaBJE7VQUQghCjsfH1WtIyZGpdVVqqR+12osDUHLPDw8WLhwIeHh4XhocIJKAmghbmf3bpg2Ta20BlWyaPhwaNDAocPKN4GBamvcOPO2xESVtpJlttp29CimpCT4+2+16XRqhrtpUxVQV67s+HQWIYRwFINBTbAkJKg0uYzc6IAAR49M5AEJoIW4mQsX4KOPYMUKdd3HB/r3VxU2ClvzEm9vqFVLbdekp6WxaeNGmh46hGHjRlVK77//1DZ7tirx1KQJ/O9/anGlBmcQhBAi3/n4ZOZGx8aq3Oj77pPZaCdXyKIAIXIhLQ0WL4Yvv1Qdp3Q6Vct54EBZSJeV0cjVSpWwNm+OoX9/9YVj40ZYvx62blWnLn/8UW3u7mrGvmlTtQUFOXr0QghRcIxGNRsdFwf//ps5Gy1/U24qMTGR0NBQ0tLSOHXqlLTyFkLTNmxQ1TWiotT1mjVVukb16o4dlzMoXhw6d1ZbSgrs3Kk+z/XrVZm/9evVBiq943//U8F09eqq8ocQQrg6Pz9VOzomJvtsdGE7q5lLFy9edPQQbkqOmBAAp07B9OlqBhXUYrjBg6FDBwnu7oaHh0rfaNIERo5UJfQyAui9ezNzqefOVfmATZqovOmGDaUNuRDCtRmNULq0CqD37FFdDCtXBo3NsDqap6cn//zzD+vXr5dW3kJoTmKiStVYvBjS09Uvtm7d4IUXJJDLKzqdmmWpWBGef16tSN+4Uc1Ob96syjz9+qvajEbVwrxZMzU7XaaMo0cvhBD5o0gR9Xfm3Dn1e7FyZShbVrrDXqPX66lRowanTp2SVt5CaIbVqhYHfvSR+vYPqurEa6+p02ki/xQtCo88ojazWVU5yZidjopS+dNbt6rKJ+XLZ1b1eOABOc0phHAtRqOaKLh6Vf0uvHhRBdJFijh6ZOI25K+RKHwOHID331cLOQBCQlTg3LSpY8dVGJlM8OCDahs2TLUcz8ib3r0bjh9X28KFKnewcWMVTIeFqetCCOEK/P3VbPTZs2o2ukoVFVgX4tlos9nMl19+yd69e6WVtxAOdfkyfPopLF8ONpsqcv/CCyplQ1qtakNGY5fnnoP4eJXisX49bNqk8gVXrlSbwaBK6jVrprb77pOa00II52YyZc5G79qlZqMrVSq0kwVpaWkMGDAAgHfeeUdaeQtR4NLTVevtzz9XBe1BLQ4cPFhVjhDa5OurOiS2bauO4X//ZaZ6HD+u/sDs2gUzZ6o/OhnBdJ06Ul9VCOG8Mmajo6Iyc6PLlCl0C9oNBgOdOnUiJiZGWnkLUeC2bVO5tMePq+tVqsCIEVC7tkOHJe6Q0aiOWe3a8MorcOaMSvXYsAF27IDTp+Hbb9Xm7Q2NGqlgunFj6folhHA+JpNKL7xyRU0UXLqkFmL7+jp6ZAXGw8ODH374QVp5C1Ggzp6FGTNUi2lQ3+gHDoRHHy3UOWUuo3Rp6NpVbYmJ6ovS+vWquselS7B6tdp0Orj//szZ6YoVJdVDCOE8ihZVkwKnTqk0xMqV1e+/QjYbrUUSQAvXkpICCxaoRWepqSpYfvppePHFQptH5vK8vaFlS7VZrWqR6IYNsG4dHDqk6k7v3avy34OCMkvkSXtxIYQzcHNTKRyXL2fPjfbxcfTICjUJoIVrsNnUjOOHH6qud6ACpOHD1ayjKBz0eqhRQ20vvQTnz2dW9di2TXX/WrZMbR4eqr14RkAt+fBCCK3S6VSDr9RUVa3o8mWVkliqlMvORiclJVG9enWSkpI4cuQIRTRW2k8CaOH8jh5Vec47dqjrwcEwdCi0aiWn6wu7EiXgiSfUlpKifkbWr1dBdUyMmqVet07tW7Wq+plp3x5KlnTsuIUQ4kbc3VVu9KVLsHNnZt1ojVWoyAs2m41Tp07ZL2uNBNDCecXFqcoay5aBxaJ+sfTsCb16yal5kZOHh5ppbtpUnbE4ciQzmP7vPzh4UG2zZkHduqpSS6tWkvojhNAWnQ4CA9WkwIkTmXWjS5Z0qUkjDw8PNm3axMaNG2URoRB5wmJRtZxnzVK1gQEeekjNOpcq5dChCSeh06lZm8qVVS3wy5fVTPTKlWpWJ6NE3tSpKuDu2BGaNJF64UII7fDwyJyN3rFD1dCvWNFlZqMNBgP169fn/PnzUsZOiHu2e7fqInjokLpevrzKc27QwKHDEk4uIAA6d1ZbdDSsWgXh4XDsmKrk8vffqnxU69YqmK5Vy2XzDoUQTiTrbPTRo5m50cHBLjUbrUUSQAun4HHpEoZZsyAiQt3g4wP9+8NTT6kawULkleBglQbUq5dK8wgPVzPTFy7ATz+prVQplSvdoQOUK+foEQshCjsPDyhbVuVEZ8xGV6jg1LPR6enpLF68mD179tC2bVtp5S3EHUlNRT9jBq2mTkWfkqK+UXfuDC+/rOpjCpGfKlWCIUNg0CCV0hEeDn/9peqMz5untmrVVCDdtq2aCRJCCEfQ6VQ1oeRk9eX//HkVRJcu7ZTdWVNTU+nduzcA48aNw9PT07EDuo4E0ELb/vkHw1tvAWC9/370o0apgEWIgmQwwIMPqm3UKLX4MDwcNm1SdacPHFAlFBs2VMF0ixZOPfMjhHBinp5qNvrqVfXF//RpFUgHBTlV6pler6dVq1ZcvHgRvQbHrb0RFZBZs2YRGhqKh4cHDRs2ZNu2bbfc/8MPP6RKlSp4enoSEhLCq6++SkpKSgGNthBr1AjLwIHsHDoUy6efSvAsHM/DA9q0UZ0uV61SAXXNmqqJy+bNMG6cmo0eO1YF2Onpjh6xEKKw0enUWdoyZVTFqm3b4J9/VI60k/D09GTFihVMnDhRc7PPUEhnoJcuXcqwYcOYPXs2DRs25MMPP6Rdu3YcOnSIEiVK5Nh/8eLFvP7668ybN4/GjRtz+PBhevfujU6nY/r06Q54B4WL9e23Ob12LTVlQYTQGn9/1eny6achKkrlSoeHq8srVqgtIADatVMz09WqycIeIUTBMRjUzHNampqJjomB++5TOdLe3o4enVMrlAH09OnT6devH88//zwAs2fP5vfff2fevHm8/vrrOfbftGkTTZo0oVu3bgCEhoby7LPPsnXr1pu+RmpqKqmpqfbrcXFxAJjNZsxmc16+HZdnvjaDZ7Za1Syf0ATztWNhlmOilC6tSuL16YNu3z50K1agj4hAd/kyfPstfPsttvvuw9q+Pdb27dX++cRljo3VSkbmpiv8/3eZ4+KCXP7YGI1q8XNSEhw+rNZxhIaq2zRcnjMjXirIuCm3r6WzabG9Sz5KS0vDy8uLZcuW0blzZ/vtvXr14urVqyxfvjzHYxYvXszLL7/MH3/8QYMGDTh+/DgPP/wwPXr04I033rjh60yYMIGJEyfe8Lm8JDdSiEJBl55Oid27KbN2LSW3bsWQlma/71K1apxu3pwzjRtjlmYtN2RISeGRZ54B4LclS7BosJmCECJ/pKamMnz4cACmTZuGu7t7gbxuUlIS3bp1IzY2Fr9b/G4udAH02bNnKV26NJs2bSIsLMx++8iRI1m7du1NZ5U/+ugjhg8fjs1mIz09nf79+/PZZ5/d9HVuNAMdEhLCxYsXb3lARE7m2FgiNm6kTdGimDSYB1VYma1WIqKjaRMcjEmDCzw0JyEB3Zo16FeuRLd9O7prv3ptRiO2xo2xduiArWlT1VHzHrnMsUlOxtS8OQDmtWvV4ign5jLHxQUVymNjsaicaLNZle8MDVUpZxqRmJhI0WvVts6fP4+/v3+BvG5cXByBgYG3DaALZQrHnVqzZg3vvvsun376KQ0bNuTo0aMMGTKESZMmMXbs2Bs+xt3d/Ybflkwmk+ZqGWretTrPJr2+8PxicyJyXHLJzw8efVRt58/DH39AeDi6w4fRrVuHft06Vd+8dWuVL12nzj2vmHf6Y5Nl7Ca93qkqCNyK0x8XF1aojo1er/KjzWaVG33xoqreUa6c+l3kYL6+vkRERLBlyxZ8fX0LLHbK7esUugA6MDAQg8FATExMtttjYmIIDg6+4WPGjh1Ljx496Nu3LwAPPPAAiYmJvPjii4wZM0aT5VWEEBpWogQ895zajh5Viw9XrFB/xH7+WW1BQSqQ7tBBlaASQoj8YDKpNRlJSer3UXS06vJbpkyenBG7WwaDgebNm5OYmKjJVt6FLvJzc3OjXr16rF692n6b1Wpl9erV2VI6skpKSsoRJGcczEKWASOEyGsVK6pGLb/+Cp9/rhoF+fioYHr+fOjaFbp1g2++Ud0QhRAiP3h5qRlogwH27IEtW+DMGZXqIXIodDPQAMOGDaNXr17Ur1+fBg0a8OGHH5KYmGivytGzZ09Kly7N5MmTAejUqRPTp0+nTp069hSOsWPH0qlTJ01+KxJCOCG9HurVU9uIEbBhg5qV3rBBrZo/fBhmzlTNXDp2hJYtpQyVECLvFSkCvr5w6ZJqCx4crM6CFStWoGU409PTWb58OTt37pRW3lrRtWtXLly4wLhx44iOjqZ27dqsXLmSoKAgACIjI7PNOL/55pvodDrefPNNzpw5Q/HixenUqRPvvPOOo96CEMKVubtDq1Zqi42FP/9UwfTu3aohwrZtMHkyNG+ugulGjexrBYQQ4p7p9aotuNms1mxcvAghISo/2te3QIaQmprK008/DcCoUaM010yl0FXhcJS4uDiKFCly21WdIifz1auEr11Lx2LFpAqHhpitVsLPnqVjqVKFZ9GNo50+nZkvfepU5u3+/qr7YceOUKMGZpvNNY5NcjI0a6Yur1/vElU4XOK4uCA5NreRnKxSyLy9VX50SEi+50cnJyfTqlUrrly5wtatWwssdsptvCZTFkII4SzKlIG+fVXDlgMHVCC9apUqRfXdd2oLCUHfvj2e9eurJglCCHGvPD1VfnRcHPz7r8qNrlABSpZUOdP58pKerF27lvDwcM3NPoME0EII4Xx0OqheXW1DhqiUjhUr4O+/ISoKw5w5tP7yS9VC/Pnn1YyREELcKz8/tcj58uXs+dGBgQWaH60FEkALIYQzMxqhcWO1JSXBmjVYf/0V/fbtKqheuRJatIA+faBaNUePVgjh7PR6FTCnp6u0josX1dmx8uVVgF1ISAAthBCuwssLOnbE0r4969eto9nvv6P/+281M/333xAWpgLpOnUcPVIhhLMzGlUKR0oKnDihSm+WK6dSPTw87vnpk5OTadasGbGxsbRs2VKqcAghhMh/VytWxPLee+hPnoSvvlKdDzdvVludOiqQbtSo0J12FULkMQ8PFTTHx8N//8HZs5n50fdQHchqtbJz5077Za2RpaZCCOHKypeHSZPghx/giSdU17F//oFXXoGePeGvv0CDf5yEEE7G11cF0mlpsHMnbN+uSuDdZbE3d3d3fv75Z958803cHdgR8WYkgBZCiMKgTBl44w1Yvlx1NvTwUJU8Ro6EZ56B8HCV0yiEEHdLp1MNV0qWVAsNt2xRXQ1jY+/4qYxGIx07dqR+/foYNVjnXgJoIYQoTEqUgGHD4LffVBqHjw8cPw7jxsGTT8KPP6oZJCGEuFtGo6rQUayYqlm/eTMcOqTqSbsICaCFEKIw8veHl19WgfTAgVC0qKrt+u678NhjsGiRS/2xE0I4gIeHOvvl6Qn798OmTRAZmauzXRaLhT///JPdu3djsVgKYLB3RgJoIYQozHx8VK3oX3+F115TM9QXLsCMGfDIIzB3rlocJIQQd8vHR3UvtFpVfvS2bapqxy3yo1NSUujYsSMTJkwgJSWlAAebOxJACyGEUDNFzz4LP/8Mb76pZo1iY2H2bBVIz5qlchqFEOJu6HQQEKA6pF65Alu3wu7dcPXqDXfX6/XUrFmT0NBQ9Bpsr669EQkhhHAcNzfo3BmWLYO331ZVPBITVSm8Tp1g2jQ1cySEEHcjIz86MFClc2zeDAcPqkZQWXh6erJjxw4+/PBDTbbylgBaCCFETkYjtG8PS5aooLl6dUhNVdcfe0wF11FRjh6lEMJZuburM11eXio/evNmOHkSzGZHjyxXJIAWQghxc3q9agW+YAF88gnUrasWAP38s6raMWYMHD3q6FEKIZyVj4+qH22zqRr1W7dCdLTm69Nrr7CeEEII7dHpVOfCRo1U3uJXX8HGjbBqldqaN1dl8WrUcPRIhRDORqdTlYD8/ODiRdi6leTAQNqPHMklaeUthBDCJdSuDTNnqrzFr75S3QzXrlVbw4aqqke9etImXAhxZwwGCAqCtDSskZGs27QJAGtKigquNUQCaCGEEHenalV47z2Vtzh/PqxYoU6/bt0KNWuqGekmTSSQFkLcGTc33ENDWTxhAv8kJuIeH69KbGqI5EALIYS4N6GhMGEC/PQTPPWUquTx778wdCh07w5//gkabIQghNAuo9HIUx070qRJE2nlLYQQwoWVKgWvvw6//AI9eqjuY4cPq9u6dFFdD3PRgUwIIbROAmghhBB5KzAQhgxR3Q379QNfXzh1Ss1SP/44fP+9KoknhBA3YbFY2LRnDwcOHJBW3kIIIQoRf3946SUVSA8apLqQnTun8qYffRS+/jpH8wQhhABISUujRb9+jB49mhQNfuGWAFoIIUT+8vGB3r1VaseIEWqV/aVLqpJHp04wZ45qGy6EENfodDoqhoRQsmRJdBpciCwBtBBCiILh4QFdu6omLGPHquYJsbHw+ecqkP7oIxVYCyEKPS8PD/b/8AOfffYZXtLKWwghRKFnMql24N9/D+++C5UqqVSOhQtVasd776lOZEIIoVHaqwsihBCicDAYoG1baNMG1q+HefPgv/9UYP3jj+o+IYTQIAmghRBCOJZOB//7HzRrBjt2qEB6+3bVmCXDxYsQEuK4MQohClRKaipPjBzJ+ZQUHlq0CG018pYUDiGEEFqh08GDD8Jnn6kW4Y0bZ97XvbualbZaHTc+IUSBsVitrNi4kZ07d2LR4P97CaCFEEJozwMPqFzoDElJKl96wACIinLcuIQQBcLNZGLuuHG88soruJm0Nv8sAbQQQghnMGgQuLvDzp3wzDOwYIF0NRTChZmMRno+8gitWrXCJAG0EEIIcRe6doWlS6FBA9XF8OOPVW3pQ4ccPTIhRCEkAbQQQgjnUKYMzJoF48ap9uAHD0LPnvDJJ5CS4ujRCSHykMViYffhwxw/flxaeQshhBD3RKdTtaK//x5atQKLBebPh27dYNcuR49OCJFHUtLSaPDccwwbNkxaeQshhBB5IjBQLTKcNk1djoyEF19UCw0TEhw9OiHEPdLpdJQqXpyAgABp5S2EEELkqRYt1Gz044+r6z/+CF26wNq1Dh2WEOLeeHl4cPL335k3b5608hZCCCHynK8vjBkDs2erZivnz8Nrr8Ho0XDpkqNHJ4RwQRJACyGEcA3168O336qFhQYDRESo2ejffgObzdGjE0K4EAmghRBCuA4PDxg8WNWJrlwZYmNhwgR45RU4e9bRoxNC5FJKairPvP46U6dOJUWDVXYkgBZCCOF6qlaFhQth4EBwc4MtW1Qt6W+/VZU7hBCaZrFa+fGvv9i0aZO08hZCCCEKjNEIzz8PixdDnTqQnAwffAB9+8KxY44enRDiFtxMJmaOGMGLL74orby1ZNasWYSGhuLh4UHDhg3Ztm3bLfe/evUqAwcOpGTJkri7u1O5cmXCw8MLaLRCCCHuWmgofP45vP46eHvD3r3QvTt88QWYzY4enRDiBkxGIwOefpqOHTtKK2+tWLp0KcOGDWP8+PHs2rWLWrVq0a5dO86fP3/D/dPS0mjTpg0nT55k2bJlHDp0iDlz5lC6dOkCHrkQQoi7otfDU0/Bd99Bs2aQnq4C6O7dVUAthBB3wOjoATjC9OnT6devH88//zwAs2fP5vdrtQZff/31HPvPmzePy5cvs2nTJvu3oNDQ0Fu+RmpqKqlZOufExcUBYDabMcuMxx0xp6erf61W0GAeVGFlvnYszHJMNMdljo3VSsa8U579/y9eHKZNQxcRgeGDD9AdP46tTx+sXbti7d8fvLzu/TVuwmWOiwuSY6M9VquVQ5GRnL14kdQaNQrsbFFuYzSdzVa4avukpaXh5eXFsmXL6Ny5s/32Xr16cfXqVZYvX57jMR07diQgIAAvLy+WL19O8eLF6datG6NGjcJgMNzwdSZMmMDEiRNz3L548WK88vEXtBBCuApDSgqPPPMMAL8tWYLFwyNPn98UF8cD8+YRsmYNAEnFi7P75Ze5UKdOnr6OEOLOpaSk8My1//9LlizBI4///99MUlIS3bp1IzY2Fj8/v5vuV+hmoC9evIjFYiEoKCjb7UFBQRw8ePCGjzl+/Dh//fUX3bt3Jzw8nKNHj/Lyyy9jNpsZP378DR8zevRohg0bZr8eFxdHSEgIbdu2veUBETmZY2OJ2LiRNkWLYtJgN6LCymy1EhEdTZvgYEz6QpkNplkuc2ySk+0X25UsCXn9/79UKZg6lfTNmzFMnoxXdDSNJ07E2rEjlqFDwd8/T1/OZY6LC5Jjoz2JyckU8fHBbLXyUI0a+FepUiCvm5ExcDuFLoC+G1arlRIlSvDFF19gMBioV68eZ86c4f33379pAO3u7o67u3uO200mkyaT4TXNqH5MTXq9/GLTIDku2uX0xybL2E16fbbreapJE5Ub/emnsHQp+vBw9Fu2wPDh0KYN6HR5+nJOf1xcmBwb7fD39ubCX38RfvYs/r6+BRY75fZ1Ct1PSWBgIAaDgZiYmGy3x8TEEBwcfMPHlCxZksqVK2dL16hWrRrR0dGkpaXl63iFEEIUAC8vFTB/+SWULw+XL8Mbb8CwYXDd3wshhCh0AbSbmxv16tVj9erV9tusViurV68mLCzsho9p0qQJR48exZplccHhw4cpWbIkbm5u+T5mIYQQBaRmTfjmG+jXT539Wr9etQP/4QdZxCyEsCt0ATTAsGHDmDNnDgsWLODAgQMMGDCAxMREe1WOnj17Mnr0aPv+AwYM4PLlywwZMoTDhw/z+++/8+677zJw4EBHvQXhzFJSQM5cCKFdbm7w0ksqkL7/fkhMhMmToX9/OHXK0aMTolBITUvjhYkTmTlzZraqZlpRKHOgu3btyoULFxg3bhzR0dHUrl2blStX2hcWRkZGos+SAxUSEsKqVat49dVXqVmzJqVLl2bIkCGMGjXKUW9BOKO0NDh/3p7TbS/JYzKBh4fa3N3zPN9SCHGXKlZUKR1Ll6r86F274Nln4cUX4bnnMv8vCyHyXLrFwte//26/rDWF9n//oEGDGDRo0A3vW3OtpFFWYWFhbNmyJZ9HJVyS1QqXLkFqqlr1X6GC+sOblKS22Fi4ehXi4+HiRbW/0aiC6YzA+iblEoUQ+cxggG7doHlzePdd2LoVPvkEIiJg7FioWtXRIxTCJZmMRia/8goH4+IwafDLqvZGJIQriYuDK1egWDGVWxkcnFlJIGs5Q6tVlezKCKrj49XjkpNVcG2xqMdlzFJ7eKiZayFEwShdWgXOv/8O06fDoUPQq5eaie7XT/2fFELkGTeTidd69CD87FlNrjeTAFqI/JCSAhcuqLq1NWtCSIgKfG9Grwdvb7VlsNnUrHVGUJ2YqILqxEQ1o52WptI93Nwyg2pJAREi/+h08MgjEBYG06apWegFC+Dvv2HMGKhXz9EjFEIUEAmghchL6ekqDcNigdBQVQ7rbhvn6HSZKRwBAZm3m82ZQXVysgqqY2PVbHfGQousKSDu7pICIkReKlZMLSps3x6mTIHISLXo8PHHYcgQ8PFx9AiFcHpWq5Uz589z6dKlbFXQtEICaCHygs2WmcccFKTynEuUyJ/ZYJMJihRRWwaLJXsKSEbqSFKSqmdrtWamgGRsGswpE8KpNG+uZp0/+gh+/BF++gk2bIBRo6BFC0ePTginlpyaSrlHHgHgif37ucU5XIeQv6BC3KukJDXr7OcHdeqoXMmCzk82GNSsV9aZL5tNpZJkTQG5fDkzx9psVkF11hQQNzdJARHiTvj4qIYr7drBO++o2ejhw6F1axgxQs1WCyHuitFgwOboQdyEBNBC3K30dFWWTq+HypVVykbWHGZH0+lUDranZ/Y/4mlpmUF1UpKaqY6PVzPoGfWpTabsKSDS2laIW6tXDxYvhrlz4euv4c8/Yds2ePVVlTctX0yFuCPenp4kbd5M+NmzeHt5OXo4OUgALcSdslrVTG5yMpQsqWrFOtMsk5ub2vz9M29LT89MAUlOziytl7Fg0WZTQXTW0noSVAuRnYcHDBqkZp8nTVKVOiZOhJUr1Sx1yZKOHqEQIo9IAC3EnUhIUAFlQADUqKH+ILrCAj2jEXx91ZbBas1MAUlOVrPUGV8c4uJU0K3Xq8/D11fKeAmRoWpVVZ3jm29gzhxVO7prV/T9+0OzZo4enRAiD0gALURupKaqsnTu7ipwvu8+1w8Y9Xrw8lJbVhml9eLj4Z9/1P2xsSqdxc1N5YR6eckMtSjcjEbo3Rseegjefht27cLw4Yc0Cw9XudLlyjl6hEJoWmpaGkOnT+dUYiKtPvwQrXU+kL9wQtyKxQIxMWqRYNmy0KgRVKni+sHzrbi7Q9GimaejGzWCpk2hfn3VKCYtDc6cgdOnVRpIerpDhyuEQ5UtC7NnwxtvYPP2JuDwYYy9esGqVY4emRCalm6xMHvZMlasWCGtvIVwKlevqlSF4sVVWbqgIJlVvRGdTlUg8fNTDWNSUtRnd/my+vJx/rwKoj09JdVDFE56PTzxBOlhYcS+/jqB+/apxiu7dsGwYbdusiREIWUyGnmzb1+OxMdLK28hnEJyskrX8PGBWrWgTBmVmiByx8NDzUQHB6vqJHFxKsUjOloF1ufPqyofPj6qaol8KRGFRVAQm956i4d//x3DV1/BDz/A3r2qGUvZso4enRCa4mYyMe7FF6WVtxCal56uAmdQM87ly0tHsXtlNKoFlwEBqsxfQoIKpi9eVNvZs2o/b2/1WRd0/WwhCpjNYMDavz+GunVh7Fg4fBieew7efBPatnX08IQQuSQBtBA2W2aDkeBgFTwHBkrd1rym02VW+ihTRi1GjI1Vdaijo1VAbTarGWwfH5XyIcdAuKpGjVTd6IxUjjfegJ07JaVDiGtsNhtX4+NJSEjAZtNeOxUJoEXhllGWrkgRqFsXSpWSFtcFxd1dtTsvUULV0s6a6pExS200ZqZ6uEK5QCGyKl4cPv0UvvgCJKVDiGySuAAN5QAAN/lJREFUUlIo0aoVAA/v34/WkjgkUhCFU1paZtm1atVUeoGnp6NHVXgZDKqyR9GiqkRgUpIKoi9cUIH0uXPqTIGXlwqoZYZOuAqjEV5+GerUyUzp6NFDzUxLSocQmiUBtChcrFY145yaqtIIypdXQZvQDp1OzTh7e6szAmlpmZ0Rz51TKR+pqZmpHl5ekuohnF9YmErpePNNSekQAvDy8CBx0yZWnD2LlwYnuCSAFoVHXJwKvooVg5o1Vb6zVIDQPjc3dao7o5xgfLwKpmNi1PG8fFkdx4yFiJKCI5xViRKZKR3z5klKhyjUdDodJqMRo9GIToOTJPKXRri+lBSVruHlpQLnkBCZ0XFWer3KVy9SJHuqx6VLKqCOjlapHp6eKpiWmtPC2UhKhxBOQQJo4brS01X+rMWi2uaWL6+afQjXkdFqvGRJ1SEyI9UjYyGitBcXziojpWPMGPjnH5XSsWsXvPqqTACIQiHNbGb0rFkcT0ig9dSpmmvlLQG0cD02mwqi4uNV98AKFdSpUQ2eAhJ5yGRS5QcDAzNTPTKC6EuXVLqHTqeCaUn1EM6gRAn47DP4/HNVpWPZMvj3X3jvPXUmTQgXZk5PZ/o336jL777r4NHkJH9BhGtJSlKVG4oUUadAS5eW5hyF0fXtxZOTVTAt7cWFszEaYeBA9fts3LjsjVfatHH06ITINyajkWHPPcfxhARp5S1EvjGbVeCs16tT+aGhalGZEKACZU/P7O3Fr0/1MJlUMO3tLWcrhPY0bgyLFqmUjt27YfRoldIxdKikdAiX5GYyMWXwYGnlLUS+sFrVrGJyssqDrVhRVdkQ4maythcvVy57e/ELF9TPk5ubOouhwdJJohALCoLZszNTOr7/XqV0TJkiKR1CFDBZUSOcV3w8REWp2Zf69dUmwbO4ExntxcuUgdq1oWlTaNBA5Z4mJMCpUyqoTk119EiFUDJSOj76SH3JO3RIpXT8+aejRyZEnrLZbJjT00lPT5dW3kLkidRUFdS4u0ONGqqcmeSwirzg6any5kuXVl/QLl+GM2fUAkSzWaV3+PnJAkTheI0bZ1bp2L0bXn8dnn5aUjqEy0hKScGnWTMArkgrbyHugdWqFoClp6umAuXKgb+/o0clXJWvr9pCQjJrTZ85o34GbbbM+6U0nnCUG6V0ZDReKVPG0aMTwqVJAC2cx4ULmWXpgoIkcBEFQ69X7d6LFlWLU69cUT+L587B6dNqNtrPTxYfCsfIWqVj7Fg4eBC6d1eXW7d29OiEuGteHh6cX72aP86d02Qrb4lAhPZlnC6vWVPlp5YsKcGzcAyjUbUUr15d5Us3aqRm+lJSIDJSzU6npDh6lKIwykjpqF0bEhNVSsd770FamqNHJsRd0el0+Pv64uPjo8lW3hKFCO3LyOcLDVXVEYTQAnd39WWuTh0VTD/4oKrsERurgumLF1XetBAFJSOlo3dvdf3776FPH3WmRAiRpySAFkKIe+XtrXKlGzaEJk3ULKCXl0r1iIpSNactFkePUhQGRiMMGgQzZ6oqHRkpHVKlQziZNLOZt774gm+//ZY0DZ5JkQBaCCHyik6ngpZy5dQp9caNoVo1dfu5c2oRYny8WoQoRH5q0kSldNSqlZnSMXWqpHQIp2FOT+ftuXNZunQp5vR0Rw8nBwmghRAiPxgMqi55lSrQrJnKly5XTqV1REWpLohJSY4epXBlQUGqQkdGSsd338ELL0hKh3AKRoOB/k89RYcOHTAaDI4eTg4SQAshRH4zmVQw88ADmc1agoPVzKA0axH5KSOl48MP1dmRAwdUSsfq1Y4emRC35O7mxkcjR/LSSy/hrsHa5hJACyFEQcpo1lKvngqm69VTZfCuXFGLDy9fVrXOhchLTZvCokWqmlFiIowaBe+/LykdQtwlCaCFEMJRfHxUJ81GjVTO6gMPqEozMTHqNHtsrGogJEReCA6GL76AXr3U9aVLJaVDiLskAbQQQjiaXq+6alasqALpxo2hUiW12PDMGbUlJMjiQ3HvjEZ45RVJ6RCal5icjFdYGE8++SSJGlwvIgG0EEJoidEIgYGqWUvG4sOyZVWDlozFh9KsRdyrG6V0TJsmKR1CU9ItFiwaLQEqrbyFEEKr3NzUaffgYFWx49IlVQ7v4kWV5uHtrWYRTSZHj1Q4o4yUjk8/hYULYckS+PdfePdd1WFTCAfydHfnxG+/8VdMDJ4eHo4eTg6FdgZ61qxZhIaG4uHhQcOGDdm2bVuuHrdkyRJ0Oh2dO3fO3wEKIURWXl6qWcuDD6rZwzp1VAAtzVrEvTAaYfDgzJSO/fvhuefgr78cPTJRyOn1ekqXKEGxYsXQ67UXrmpvRAVg6dKlDBs2jPHjx7Nr1y5q1apFu3btOH/+/C0fd/LkSYYPH06zZs0KaKRCCHEdnU5V7cho1tKkiWrWoter2enTp1W+tBB3ImtKR0ICjBwpKR1C3EKhDKCnT59Ov379eP7556levTqzZ8/Gy8uLefPm3fQxFouF7t27M3HiRMqXL1+AoxVCiJvQ6yEgQDVradoUwsKgfPnMMngXL0pJPJF7GSkdPXqo60uWQN++ahGrEAUszWzmg6+/5qefftJkK+9ClwOdlpbGzp07GT16tP02vV5P69at2bx5800f99Zbb1GiRAleeOEF1q9ff9vXSU1NJTVLY4S4uDgAzGYzZrP5Ht5B4ZPxecnnpi1yXDSoaFEoWhRzSAisXYvZy0vNSptMKtA2OtmvfKuVjOxus9Xq9CX9zNfGb9by+9Dr4ZVX0NWujWHiRHT792Pr3h3L2LHYWrZ09OjyjVMcm0ImKS2N0R9/DMD7w4fjVkB/a3L7N83Jfpveu4sXL2KxWAgKCsp2e1BQEAcPHrzhYzZs2MCXX37J7t27c/06kydPZuLEiTlu/+OPP/Dy8rqjMQslIiLC0UMQNyDHRbsirl5VKR/p6XCbFDUtMqSk8Mi1y6vOncOiwYVEdyMiOtrRQ7i9ChXw/OAD6k+bRsChQxhHjeLYI4+wr1cvbC68aNUpjk0hYTabaXntS9vagwcxHTtWIK+blMuSeYUugL5T8fHx9OjRgzlz5hAYGJjrx40ePZphw4bZr8fFxRESEkLbtm3x8/PLj6G6LLPZTEREBG3atMHkwr+4nY0cF+3KcWwsFpXOERmpFh3qdGpGWoPtcbNJTrZfbFeypOri6MTMVisR0dG0CQ7GpMFFUTmUKgVffYXl008xfPMNFX77jXLHj2N55x3VTdOFON2xKSQ6Tp6sjssDD2AKCSmQ18zIGLidQhdABwYGYjAYiImJyXZ7TEwMwcHBOfY/duwYJ0+epFOnTvbbrNdO8RiNRg4dOkSFChVyPM7d3f2GvdtNJpMEG3dJPjttkuOiXfZjYzKpsmSlSqlA+tQpVU/aZoNixUCrM7tZAhmTXp/tujMz6fXOE6S5ucHQoVC3LkyYgH7/fvQ9e8L48dCihaNHl+ec6tgUIiaDocD+zuT2dQrdT4mbmxv16tVjdZauS1arldWrVxMWFpZj/6pVq7J37152795t3x599FFatmzJ7t27CSmgb0RCCHHP9HooUQLq11cLDkNCVPm706dVnWkhbuZ//4PFi1W7+fh4GD4cPvgAZA2EKKQK3Qw0wLBhw+jVqxf169enQYMGfPjhhyQmJvL8888D0LNnT0qXLs3kyZPx8PDg/vvvz/Z4f39/gBy3CyGEU9DpVLfDYsXgvvtUHekzZ1SjlqJFwcfH0SMUWhQcDHPmwKxZ8PXX8O23sG8fTJ2qfp6EyEOJycmU7tABs9XKmR078Hf0gK5TKAPorl27cuHCBcaNG0d0dDS1a9dm5cqV9oWFkZGRmizaLYQQeSojFzogQAXSp0+r7coV8PcHX19Hj1BojdEIQ4aoRj7jx6vOhc89B++/r2anhchDsRquaV8oA2iAQYMGMWjQoBvet2bNmls+dv78+Xk/ICGEcCR/f7WVLQtnz6oFh5GRqjudn58KtoXI8L//qfbfr70Gx4/Diy/CqFEgXXpFHvF0d2ffsmWsPX9eWnkLIYTQOD8/qFpVdTmsUUPVXo6MVLnSNpujRye0JCQEvvoKWrZUudBvvw3vvSd50SJP6PV6KpUtS6lSpTSZFaC9EQkhhHA8Hx+oXFkF0rVqqdsiI+HyZadvaCLykLe3Cpr791dnKb7/Hl5+Wf2cCOHCJIAWQghxc15eUKECNGmiSpmZTGrR4cWLqr60EHq9avn9wQcqoP7nH9UOfP9+R49MODFzejqfff894eHhmux4KwG0EEKI2/PwgNBQNSNdv766fuaM6nCYnu7o0Qkt+N//YMECtSA1JkYF1b/95uhRCSeVZjYz5P33+eKLL0iTAFoIIYRTc3NTCw0bN4YHH1SVOs6dU41ZJJAWoaEqiG7WDNLSYMIENTMtPxviDhn0ep546CEaN26MQYM50IW2CocQQoh7kNHdMDhYzUJndDc0GFR9aTc3R49QOIqPjwqav/gC5s5V9aKPHoXJk1WlFyFywcPdnSVTphB+9iweUoVDCCGESzEaVYvwBg2gYUMoXhwuXFDpHSkpjh6dcBS9Xi0sfP99lUe/fbvKiz50yNEjEyJPSAAthBDi3hkMajb6wQehUSMoXVo1ZDl9WgLpwqxlS1XqLiREpfr06QOrVjl6VELcMwmghRBC5B29HkqUUBU7GjVS+dJXr6pAOinJ0aMTjlChgsqLbtwYUlNhzBj46COp4iJuKSklhdCHH6ZPnz4kJSc7ejg5SAAthBAi7+l0EBgItWurwKlcOUhIUCXwNNyeV+QTPz+YMQN691bXFy5ULcFjYx06LKFdNpuNsxcucPnyZWwabOIkiwiFEELkH50OihZV2333qZno06dVow1/fxVYicLBYIBBg6BKFZg4EbZsgZ491YLDihUdPTqhMR5ubmz75hs2nD+Ph7u7o4eTg8xACyGEKBhFiqj24I0bQ7VqquXzqVPSJrywadMG5s1Ti0/PnIHnn4fVqx09KqExBoOB2pUrU758eQwGg6OHk4ME0EIIIQqWry9Uraq6Gz7wgAqeT51Siw6lTXjhULmySuNo0ACSk2HUKPj0Uzn+wmlIAC2EEMIxvL2hUiUVSNeurdI9oqLg0iUJpAoDf3+1mPC559T1efNg2DCIj3fosIQ2mNPTWfjbb6xevVpaeQshhBA5eHqqSg1NmqjqHW5uKk/60iVHj0zkN6MRhg6Ft94Cd3fYsAF69YITJxw9MuFgaWYzfd96i48//lhaeQshhBA35eGhWkE3bgz16qmAKoOUPHNtHTuqroVBQRAZqap1rF3r6FEJBzLo9XRo0oR69eppspW39kYkhBCicHNzU/WjGzXKvO3MGTm17+qqVYOvv1ZnIRIT4bXXVDtwSecplDzc3Vk+YwZjx46VVt5CCCFErplMmZfvv191NDx9GtLTHTcmkb8CAtRiwq5d1fUvvoARI6R2uNAcCaCFEEJoX4UK0LChahd+9qwqfSdck9GoguZx49SXqLVrVam7U6ccPTIh7CSAFkII4RyKFlW50XXqqJzoqChIS3P0qER+efRRmDMHihdXiwp79VKLDEWhkJSSQvUnn2TAgAHSylsIIYS4J0ajWmjYqBGEhMD586pahzRicU3336/yomvWVGkcr74KX30lx7sQsNlsHI2K4ty5c5ps5S0BtBBCCOfj56dmouvWVS2iIyNVjrRwPYGB8Pnn8MQTKnCeNQtefx2Skhw9MpGPPNzcWDNnDpMnT5ZW3kIIIUSe0evVLHSjRipH+tIlNSMtVRtcj8kEb7wBo0ersxCrV0OfPmpRqXBJBoOBxrVqUa1aNWnlLYQQQuQ5b291iv/BB8HLS+VGy+yka3rySZg9G4oVg6NHoWdP2LLF0aMShZAE0EIIIZyfTgclS0KDBlClCsTGQnS0NGBxRbVrq7zoGjUgLg4GD1bXNZgnK+5eeno6y/78k40bN5KuwdKVEkALIYRwHZ6eUL26CqSLFFGn+KWGsOspUULViO7USaXszJwJY8dKHrwLSTWb6fbGG7z//vukarDajgTQQgghXItOpwKsBg3ULGVSkqodrcFZLHEP3N1VreiRI9VC0pUr4YUX4Nw5R49M5AG9Tsf/6talRo0a6KWVtxBCCFFA3NxUOkeDBqqW8NmzKrVDuA6dDrp0Ud0L/f3h0CHo0QN27HD0yMQ98vTw4M/Zs3nnnXfwlFbeQgghRAErVgzq11cLDc1mldZhNjt6VCIv1asH33wDVauqLpUDB8KSJZIXLfKNBNBCCCFcn9GoSt01agSlSqnT/JcvO3pUIi8FB8PcudChg1o8Om0aTJwIqamOHplwQRJACyGEKDyKFFHNV+rVU6f/o6IkwHIlHh7w1luqY6FeD7/9Bv36QUyMo0cm7lBySgr1u3dn6NChJGtwcagE0EIIIQoXgwHKllWz0aGhcOECXLwop/tdhU4H3bvDxx+rL0z796u86N27HT0ycQesNhv/HjnCyZMnsWqwOZIE0EIIIQonHx+VF12/vqroEBkJycmOHpXIKw0bwsKFUKmSStd56SVYtky+KDkJDzc3wj/+mAkTJkgrbyGEEEJT9HooXVpV6qhUCa5cUaf7NTjjJe5C6dIwbx60aaPyoqdMgXffBQ3WFRbZGQwGWjdsSO3ataWVtxBCCKFJXl5w//0qkPb1VbnR0oDFNXh6qqB50CCV3vHTT9C/v0rdEeIuSQAthBBCgAqugoJUEF29ugqgz56VduCuQKeD3r1Vx0JfX/j3X5UXvXevo0cmbiI9PZ3wDRvYsWOHtPIWQgghNM/dXdUTbtgQAgNV3ei4OEePSuSFxo1VXnT58mrh6Isvws8/O3pU4gZSzWY6DxvG22+/La28hRBCCKcRGJjZgCU1VQXSGpwJE3coJAS++gpatlQNdd5+G/3UqeikuY6m6HU66lWrRsWKFaWVtxBCCOFUTCaoWFHNRgcHq5SOq1cdPSpxr7y94b33VC40YFi2jKZvvqkqsQhN8PTwYPOCBUybNk1aeWvJrFmzCA0NxcPDg4YNG7Jt27ab7jtnzhyaNWtG0aJFKVq0KK1bt77l/kIIIVxM0aKq+UqdOqpCR1SUVHJwdno99O0L06dj8/Ym4NAhjN27qxbgUoVF3EahDKCXLl3KsGHDGD9+PLt27aJWrVq0a9eO8+fP33D/NWvW8Oyzz/L333+zefNmQkJCaNu2LWfOnCngkQshhHAYo1E1XmnYUDViOX8eLl2SusLO7n//I33xYi7UrIkuNVW1AO/fH+RvvLiFQhlAT58+nX79+vH8889TvXp1Zs+ejZeXF/Pmzbvh/osWLeLll1+mdu3aVK1alblz52K1Wlm9enUBj1wIIYTD+flB7dpqRtpgULPRGmw1LO5AyZJsmjABy8iRqh34rl3wzDPSeMWBklNSaN63L6+//romW3kbHT2AgpaWlsbOnTsZPXq0/Ta9Xk/r1q3ZvHlzrp4jKSkJs9lMQEDATfdJTU0lNTXVfj3u2gpus9mMWRYq3JGMz0s+N22R46JdLnNszGZM9otmteBLS4KCVEm0Eyfg1CmVL12smEoNuAHztbQAs6QHaI7ZagW9ntQnnsAUFobhrbfQ//MPTJmC9e+/sbz5pjreosCkWixs/vdfdbkAY6fcvo7OZitcX63Onj1L6dKl2bRpE2FhYfbbR44cydq1a9m6dettn+Pll19m1apV7Nu3D4+bJLZPmDCBiRMn5rh98eLFeHl53f0bEEKIQsKQksIjzzwDwG9LlmDR4EIi4aKsVsr/9hvVv/kGQ1oaZi8v/uvTh8hWrVRNaZHvLBYL27dvB+DBBx8ssG6ESUlJdOvWjdjYWPz8/G66nwTQ1+Q2gJ4yZQpTp05lzZo11KxZ86b73WgGOiQkhIsXL97ygIiczGYzERERtGnTBpPJdPsHiAIhx0W7XObYJCZiKloUAPOVK6pygpalpmbORoMqg5flj77ZaiUiOpo2wcGYNFiWqzC76bE5dQrDxIno//sPAGuTJljeeAOKF3fQSAsX+3F54AFMISEF8ppxcXEEBgbeNoAudCkcgYGBGAwGYmJist0eExNDcHDwLR87bdo0pkyZwp9//nnL4BnA3d0dd3f3HLebTCbn/oPmQPLZaZMcF+1y+mOTZewmkynbdU0ymVQ78KAgOHxYlbwrVgx8fLLvptdLAK1ROY5NuXLw5ZfwzTcwezb6jRvRP/MMjBgBHTrIbHQBMRkMBfa7LLevU+j+B7u5uVGvXr1sCwAzFgRmnZG+3tSpU5k0aRIrV66kfv36BTFUIYQQzkangxIlVDvwGjUgKUkF0tKAxXkZDNCrFyxapFq8x8fDuHEqiL50ydGjc1kWi4W1O3eyd+9eLBaLo4eTQ6ELoAGGDRvGnDlzWLBgAQcOHGDAgAEkJiby/PPPA9CzZ89siwzfe+89xo4dy7x58wgNDSU6Opro6GgSEhIc9RaEEEJomZsbVKmiSt4VL66C6NhYR49K3Ivy5WHePFXizmiENWugSxeIiHD0yFxSSloabQYMYOzYsaRkSYnVikIZQHft2pVp06Yxbtw4ateuze7du1m5ciVB11bYRkZGcu7cOfv+n332GWlpaTz11FOULFnSvk2bNs1Rb0EIIYQzCAhQ7cBr1cqchdZaNRGRe0ajar6ycCFUrqy+FI0erTbpUJmndDod1cqVIyQkBJ0GU2UKXQ50hkGDBjFo0KAb3rdmzZps10+ePJn/AxJCCOGajEY1e1mkCGzYANHRN8yNFk6kcmVYsEDlR3/1lZqF3rkT3ngDWrRw9OhcgpeHB3uWLiX87Fm8PD0dPZwcCuUMtBBCCFHgMlb016gBCQkQEyNNOpyZyaTSOb76Sn1BunwZhg+HsWPhWu8H4bokgBZCCCEKUvny8OCDqixfVJSkdDi76tXh66/VQkO9HlasgK5d1dkG4bIkgBZCCCEKWokSKje6bFm1wFAWpTs3d3d45RWV0lG2LFy4AEOHwltvybG9S8kpKXQYNIjx48drspW3BNBCCCGEI3h7q8WFNWtKSoereOABWLwYunVTJQ1/+UXNRueiy7HIzmqzsXrbNvbs2YPVanX0cHKQAFoIIYRwFKMRKlZUs9FeXpLS4Qo8PGDYMPjiCyhdWn0xGjgQJk9WdcFFrribTMx/6y1effVV3N3cHD2cHCSAFkIIIRwtKEjlRUtKh+uoUweWLIGnn1bXf/gBnn1WVesQt2U0GunWvj3NmzfHaNRe0TgJoIUQQggtkJQO1+PpCaNGwaefQnAwnDkDL70E06aBBvN6Re5JAC2EEEJohaR0uKYGDdRs9OOPq+tLlqg86T17HDsuDbNYLOzYv58jR45IK28hhBBC5IKkdLgeHx8YMwY++khVYYmMVF0NZ84EDbaqdrSUtDQa9+7NiBEjpJW3EEIIIXIpI6XjgQckpcOVNG4MS5fCI4+o4/n11/Dcc7Bvn6NHpik6nY77SpakePHimmzlLQG0EEIIoVVGI1SqJCkdrsbXFyZMgA8+UG3dT5yAPn1g1ixIS3P06DTBy8ODI8uXM2fOHGnlLYQQQoi7kDWl49w5SelwFc2bq9nodu3AYlFtwXv2hIMHHT0ycRsSQAshhBDOICOl4/77JaXDlfj7wzvvwHvvqctHj6q24F98Aenpjh6duAkJoIUQQghnISkdrqtVK/juO2jZUs1Gf/EF9O6tAupCKCU1lSeHD+fdd98lRYMl/ySAFkIIIZyNpHS4poAAmDoV3n4b/PxUKkePHiq1o5DNRlusVn5dt45t27ZhkVbeQgghhMgT16d0nD8vKR2uQKeD9u3VbHSzZuoMw6xZquTdyZOOHl2BcTOZ+OyNN3j55ZdxM5kcPZwcJIAWQgghnFXWlA5PTzh9WlI6XEVgIEyfrqp1+PjAf/9B9+7wzTcqxcPFmYxGXujcmbZt22KSAFoIIYQQeS4oSAXRISGS0uFKdDpVL3rpUggLUw1XPvxQtQOPinL06Ao1CaCFEEIIV+DjIykdriooSHUwHDNGLR7dvRuefVYF1hrMD84LVquVfceOERkZiVWD71ECaCGEEMJVSEqH69Lp4PHHYckSdXxTUuD99+Hll1W7dxeTnJpKnWefZfDgwSRLFQ4hhBBC5DtJ6XBdpUrBp5/CyJHg4QE7dsAzz8CPP7rcGYdAf3/8/PwcPYwbkgBaCCGEcEWS0uG69Hro0gW+/RZq14akJHj3XXjlFYiOdvTo8oS3pydn//iDhQsX4u3l5ejh5CABtBBCCOGqJKXDtYWEwOefw6uvgrs7bNkCXbvC8uUumxutFRJACyGEEK5OUjpcl8GgytstWqTONiQmwqRJ8Pzz8O+/jh6dy5IAWgghhCgMMlI6atSQlA5XFBoKc+fC4MGqUse+fdCnD7zxhvrS5GRSUlPpOXYs06dPl1beQgghhHAgoxEqV4Z69dQCNEnpcC1GI/TsqRYUPvaYqtzxxx/w1FNq4WFSkqNHmGsWq5Ulq1axbt06aeUthBBCCA0IDoYHH1QpHWfPSkqHqwkMhLFj4euvoW5d1YBl3jx44gn45RenyI92M5mY9uqr9OnTR1p5CyGEEEIjslbpiI+XlA5XVLWqWmT4/vtQpgxcvAhvvaVmqXftcvTobslkNDL42Wd59NFHpZW3EEIIITQkI6Wjfv3MlI70dEePSuQlnQ5atoTvvoMhQ8DbGw4ehBdfVLWkT5929AidkgTQQgghRGGXkdJRujScOSMpHa7IzQ169ICffoInn1S1pP/6C55+WrUJ19gxt1qtnDx7lpiYGGnlLYQQQgiN8vGBOnUkpcPVBQTA6NGweDE0bKgWkS5cqNqE//gjWCyOHiGgWnlX7tyZl156SVp5CyGEEELDrk/piIqSlA5XVbEifPIJfPghlC0LV66obobPPQfbtjl6dAB4eXjg7u7u6GHckATQQgghhMguI6WjTBlJ6XBlOh00baryo197Dfz84MgRePllGDYMTp1y2NC8PT25um4dS5culVbeQgghhHASGSkdNWpISoerMxrh2WdVCkfXrqq74bp10KULTJ8OcXGOHqHmSAAthBBCiBszGqFKFUnpKCz8/WHECFiyBJo0UfnQixer/OjvvpNjn4UE0EIIIYS4NUnpKFzKlYOZM+Hjj6F8eYiNhalToVs32LSpQIaQmpZG/3feYdasWaSmphbIa94Jo6MHIIQQQggnkJHSkZEnm5ysOt7pdI4emcgvYWHqi9PPP8Nnn8Hx4zB4MDRuDK++qgLtfJJusTBv+XL7Za2RGWghhBBC5E7WlA53d2m8UhgYjfDUUyqIfu45dX3TJnjmGTUrffVqvrysyWhkYv/+dO/eHZNRe/O9EkALIYQQ4s5kpHSUKqVSOjRYp1fkMV9fGDpU5UK3aKHyo7/7TuVHL16s6knnITeTidF9+vD000/j5uaWp8+dFwptAD1r1ixCQ0Px8PCgYcOGbLtNzcPvv/+eqlWr4uHhwQMPPEB4eHgBjVQIIYTQIB8fqF1b1Y2+eFHlyQrXV7YsTJumUjoqV1YVWqZPV9U71q0rNJVaCmUAvXTpUoYNG8b48ePZtWsXtWrVol27dpw/f/6G+2/atIlnn32WF154gX/++YfOnTvTuXNn/vvvvwIeuRBCCKEhbm5QvTrUrAmpqRATU2gCqELvwQfh66/hzTdVd8PISFU7euBAlSN/j2w2GxeuXCE2NhabBn+mCmUAPX36dPr168fzzz9P9erVmT17Nl5eXsybN++G+8+cOZP27dszYsQIqlWrxqRJk6hbty6ffPJJAY9cCCGE0Bi9Xi0mq1cPvLwkL7owMRigc2dVP7p3bzCZVBfD7t3hnXfg8uW7fuqklBRKt2tHr169SEpOzrMh5xXtZWXns7S0NHbu3Mno0aPtt+n1elq3bs3mzZtv+JjNmzczbNiwbLe1a9eOn3/++aavk5qamq3sSuy1U1uXL1/GnMd5Qq7ObDaTlJTEpUuXMJlMjh6OuEaOi3a5zLFJTCRj9OZLl5w+z9ZljsvNGAwqkD5yBA4fhuLF1UJDJ2C22dSxiY/HJFVF7k7PntCmDYYvvkC/di389BO2VauwPvcc1iefVMH1HUjM8v/9cmwslkuX8nrENxQfHw9w21nvQhdAX7x4EYvFQlBQULbbg4KCOHjw4A0fEx0dfcP9o6Ojb/o6kydPZuLEiTluL5ePJV+EEMJl3Xefo0cghLhTSUnwxRdquwcVwsLyaEC5Fx8fT5EiRW56f6ELoAvK6NGjs81aW61WLl++TLFixdDJt9s7EhcXR0hICFFRUfj5+Tl6OOIaOS7aVRiOzddff83IkSN57733aNKkCWlpaezbt4/9+/czbtw4+2QJwNatW+nRowc7d+7E19cXAA8PD4oUKcLnn3/O66+/ztChQ+natSsmk4nw8HAmTZpEv379eOeddwA4deoUNWvWZPny5VSrVo0zZ84watQoTp48yfbt2/H397/tmAvDcXFWcmy0yRHHxWazER8fT6lSpW65X6ELoAMDAzEYDMTExGS7PSYmhuDg4Bs+Jjg4+I72B3B3d8f9ulNXufkFK27Oz89PfrFpkBwX7XLlYxMREUGXLl0YNGiQ/baGDRvaL2d932fOnAGgfPny2X4PR0VFMWbMGIYOHcoHH3xgv71OnTr4+fkxePBgnnvuORo2bGgPvMuWLUulSpWoVKkSM2bMoEmTJhw4cIB27drleuyufFycnRwbbSro43KrmecMhW4RoZubG/Xq1WP16tX226xWK6tXrybsJqcIwsLCsu0P6pf3zfYXQgiRv4KDg9myZQunTp266+dYtmwZZrOZ4cOH57jvpZdewsfHh2+//famj/f09ATU2hohROFS6AJogGHDhjFnzhwWLFjAgQMHGDBgAImJiTz//PMA9OzZM9siwyFDhrBy5Uo++OADDh48yIQJE9ixY0e2mQ8hhBAFZ/z48fj7+xMaGkqVKlXo3bs33333HVarNdfPcfjwYYoUKULJkiVz3Ofm5kb58uU5fPjwDR979epVJk2ahI+PDw0aNLjr9yGEcE6FLoUDoGvXrly4cIFx48YRHR1N7dq1WblypX2hYGRkJHp95neLxo0bs3jxYt58803eeOMNKlWqxM8//8z999/vqLdQqLi7uzN+/PgcKTHCseS4aFdhODYlS5Zk8+bN/Pfff6xbt45NmzbRq1cv5s6dy8qVK7P9Ds9LjRs3Rq/Xk5iYSPny5Vm6dGmOReY3UxiOi7OSY6NNWj4uOpsWq1MLIYQQd2jDhg00a9aMv/76i5YtW9pvX7NmDS1btuTKlSvZcqBnzJjBsGHDOHPmTI4FQ2lpaQQEBNC3b18+/PBDTp48Sbly5fjll1+oXr06xYoVk3UtQhRihTKFQwghhOupXr06AImJibna/8knn8RkMmVbQJhh9uzZJCYm8uyzz2a7PSQkhAoVKkjwLEQhVyhTOIQQQji3AQMGUKpUKR566CHKlCnDuXPnePvttylevHiuF3iXLVuWqVOn8tprr+Hh4UGPHj0wmUwsX76cN954g9deey1bZQ8hhMggAbQQQgin07p1a+bNm8dnn33GpUuXCAwMtFdMKlasWK6fZ+jQoZQvX55p06Yxc+ZMLBYLNWrU4LPPPrMvLBdCiOtJDrQQQgghhBB3QHKgRZ5bt24dnTp1olSpUuh0On7++eds90+YMIGqVavi7e1N0aJFad26NVu3bs22z+HDh3nssccIDAzEz8+Ppk2b8vfff2fbZ/DgwdSrVw93d3dq1659w7GsWrWKRo0a4evrS/HixXnyySc5efJkHr5b51IQx2bPnj08++yzhISE4OnpSbVq1Zg5c2aOsaxZs4a6devi7u5OxYoVmT9/fn68ZaeglePy448/0qZNG4oXL46fnx9hYWGsWrUq3963M9DKsclq48aNGI3Gm/7eKwy0dFxSU1MZM2YM9913H+7u7oSGhjJv3rx8ed/OQEvHZtGiRdSqVQsvLy9KlixJnz59uHTpUp68TwmgRZ5LTEykVq1azJo164b3V65cmU8++YS9e/eyYcMGQkNDadu2LRcuXLDv88gjj5Cens5ff/3Fzp07qVWrFo888gjR0dHZnqtPnz507dr1hq9z4sQJHnvsMR566CF2797NqlWruHjxIk888UTevVknUxDHZufOnZQoUYJvvvmGffv2MWbMGEaPHs0nn3xif44TJ07w8MMP07JlS3bv3s3QoUPp27dvoQ3WtHJc1q1bR5s2bQgPD2fnzp20bNmSTp068c8//+TvB6BhWjk2Ga5evUrPnj1p1apV/rxhJ6Gl49KlSxdWr17Nl19+yaFDh/j222+pUqVK/r15jdPKsdm4cSM9e/bkhRdeYN++fXz//fds27aNfv365c0btQmRjwDbTz/9dMt9YmNjbYDtzz//tNlsNtuFCxdsgG3dunX2feLi4myALSIiIsfjx48fb6tVq1aO27///nub0Wi0WSwW+22//PKLTafT2dLS0u7uDbmQgjg2GV5++WVby5Yt7ddHjhxpq1GjRrZ9unbtamvXrt1dvBPX4sjjciPVq1e3TZw4MfdvwIVp4dh07drV9uabb970915h5MjjsmLFCluRIkVsly5durc34aIceWzef/99W/ny5bPt89FHH9lKly59F+8kJ5mBFg6VlpbGF198QZEiRahVqxYAxYoVo0qVKixcuJDExETS09P5/PPPKVGiBPXq1cv1c9erVw+9Xs9XX32FxWIhNjaWr7/+mtatW2MymfLrLbmMvDw2sbGxBAQE2K9v3ryZ1q1bZ9unXbt2bN68OX/ejAvJz+NyPavVSnx8/C33EZny+9h89dVXHD9+nPHjx+fr+3A1+XlcfvnlF+rXr8/UqVMpXbo0lStXZvjw4SQnJ+f7+3IF+XlswsLCiIqKIjw8HJvNRkxMDMuWLaNjx455M/g8CcOFuAlu8u3z119/tXl7e9t0Op2tVKlStm3btmW7PyoqylavXj2bTqezGQwGW8mS/2/v7mOqLP8/gL/Pg3AOylFEj2HkGSQUNp8YOZESpzyFz8TYNOKQ1TRrkE7bLALJlLWVzRpM/jArZzQiqwlEKATxJKKCU2AQGqETJRvx0Aktzuf7h7/OryPgOMrDEd+v7d7kOtd9Xdfn+uzgZ/e5uY+bnDlzpt857nQlpqioSPR6vahUKgEg/v7+0t7efo9RjQ0jkRsRkbKyMlGr1fLDDz9Y2ry8vGTPnj1W/XJycgSAmEymewvsPjeaebnde++9Jy4uLnLt2rW7jmcsGc3cNDY2il6vl4aGBhG58++9B81o5iU0NFQcHR1l+fLlUllZKTk5OWIwGCQ2NnbI4rufjfbvs8zMTJkwYYKo1WoBICtXrhyyT6B5BZpGxb/3vpaXlyMsLAxRUVFoa2sDAIgIXn31Vej1epSUlODkyZNYs2YNVq5cidbW1kHPcfXqVbz88sswGo2oqqpCcXExHBwcEBkZCeHDZwY0lLk5f/48Vq9ejaSkJISEhIx0KGPKSOfliy++QHJyMjIzM6HX64c1tvvdcOemt7cX69evR3JyMry9vUc0tvvZSLxnzGYzFAoFDh8+jAULFiA8PBx79+7FZ599xqvQdzASuamrq0N8fDwSExNx+vRp5OXlobm5GZs2bRqaIIakDCcaAAZx/5OIyMyZMy1XJI8fPy5KpVI6Ojr69ElJSelz7kBXYhISEsTPz8+q7dKlSwJAKioqBh/EGDXcuamtrRW9Xi9vvvlmnzGffvppiY+Pt2r75JNPRKfT2RbEGDSaeflXRkaGaLVayc7Otj2AMWy0ctPe3i4ARKVSWQ6FQmFpKygouLfA7nOj+Z6JiYmRRx991Kqtrq5OAEhjY6ONkYw9o5mb6OhoiYyMtGorKSkRAHLlyhUbI+mLX6RCdsFsNuPGjRsAAJPJBABQKq0/IFEqlTCbzYMe02Qy9RlDpVJZ5qPBuZvc1NbWYunSpTAajdi9e3efMf39/ZGbm2vVduzYsUF/gxwNT14AICMjAxs2bMCXX36J5cuXD9Pqx7ahzo1Op8O5c+es2tLS0lBYWIisrCx4eHgMRxhjznC8ZwICAvDVV1+hu7sbEyZMAHDrEWxKpRLu7u7DFcqYMxy5MZlMUKuty9x/awAZik+h77kEJ7pNV1eXVFdXS3V1tQCQvXv3SnV1tfz666/S3d0tO3bskIqKCmlubpZTp07JCy+8II6OjnL+/HkRufUXuK6urhIRESE1NTXS0NAg27Ztk3HjxklNTY1lnp9//lmqq6tl48aN4u3tbZnzxo0bIiJSUFAgCoVCkpOTpbGxUU6fPi2hoaFiMBge2PtsRyI3586dk6lTp0p0dLS0trZajra2Nss6Ll68KE5OTrJ9+3apr6+X1NRUUalUkpeXNyr7MtrsJS+HDx8WtVotqampVn3++OOPUdkXe2Avubndg34PtL3kpaurS9zd3SUyMlJqa2uluLhYvLy85KWXXhqVfbEH9pKbgwcPilqtlrS0NLlw4YKUlpaKn5+fLFiwYEjiZAFNQ+7HH38UAH0Oo9Eof/31l6xdu1amT58uDg4O4ubmJqtWrerzBwRVVVUSEhIikydPFmdnZ1m4cKHk5uZa9QkMDOx3nl9++cXSJyMjQ+bPny/jx4+XqVOnyqpVq6S+vn4ktsEujURukpKS+p3DYDD0Wcu8efPEwcFBPD095eDBgyOwA/bJXvIy0HvKaDSO0E7YH3vJze0e9ALanvJSX18vQUFBotVqxd3dXbZu3frAXqQRsa/cfPTRRzJr1izRarXi5uYmzz33nFy+fHlI4uRXeRMRERER2YBP4SAiIiIisgELaCIiIiIiG7CAJiIiIiKyAQtoIiIiIiIbsIAmIiIiIrIBC2giIiIiIhuwgCYiIiIisgELaCIiIiIiG7CAJiIim8XGxmLNmjXDNv6SJUvw+uuvD9v4RET3Qj3aCyAiIrrdkSNHMG7cuNFeBhFRv1hAExGR3Zk8efJoL4GIaEC8hYOIyI5lZWVh9uzZ0Gq1cHV1RVBQEP78808AQFVVFYKDgzFlyhRMnDgRgYGBOHPmjNX5CoUC6enpWLFiBZycnODj44OKigo0NTVhyZIlGD9+PBYtWoQLFy5Yztm5cyfmzZuH9PR0PPLII3ByckJUVBQ6OjoGXKfZbEZKSgo8PDyg1Woxd+5cZGVl3TG2tLQ0eHl5QaPRYNq0aYiMjLS89t9bOIqKiqBQKPocsbGxlv7fffcdfH19odFo4OnpieTkZPzzzz+D3WYiIpuwgCYislOtra1Yt24dNmzYgPr6ehQVFSEiIgIiAgDo6uqC0WhEaWkpTpw4AS8vL4SHh6Orq8tqnF27diEmJgY1NTV4/PHHsX79emzcuBE7duzAqVOnICJ47bXXrM5pampCZmYmjh49iry8PFRXV2Pz5s0DrjUlJQWff/459u/fj9raWmzZsgXR0dEoLi7ut/+pU6cQFxeHd955Bw0NDcjLy8PixYv77bto0SK0trZajsLCQmg0Gkv/kpISxMTEID4+HnV1dUhPT8enn36K3bt3D3qviYhsIkREZJdOnz4tAKS5uXlQ/Xt7e8XZ2VmOHj1qaQMgCQkJlp8rKioEgBw4cMDSlpGRIRqNxvJzUlKSqFQquXz5sqXt+++/F6VSKa2trSIiYjQaZfXq1SIi0tPTI05OTlJeXm61nhdffFHWrVvX71q//vpr0el00tnZ2e/rgYGBEh8f36f9+vXr4unpKZs3b7a0LVu2TPbs2WPV79ChQ+Lm5tbv2ERE94r3QBMR2am5c+di2bJlmD17NkJDQxESEoLIyEi4uLgAAK5du4aEhAQUFRWhra0Nvb29MJlMaGlpsRpnzpw5ln9PmzYNADB79myrtp6eHnR2dkKn0wEAZsyYgYcfftjSx9/fH2azGQ0NDXjooYesxm9qaoLJZEJwcLBV+82bNzF//vx+YwsODobBYICnpyfCwsIQFhaGtWvXwsnJacD9+Pvvv/Hss8/CYDBg3759lvazZ8+irKzM6opzb28venp6YDKZ7jgmEdHdYAFNRGSnVCoVjh07hvLycuTn5+Pjjz/GW2+9hcrKSnh4eMBoNOL333/Hvn37YDAY4OjoCH9/f9y8edNqnP8+zUKhUAzYZjab72qd3d3dAICcnByrohsAHB0d+z3H2dkZZ86cQVFREfLz85GYmIidO3eiqqoKkyZN6vecV155BZcuXcLJkyehVv//f1/d3d1ITk5GREREn3M0Gs1dxUREdCcsoImI7JhCoUBAQAACAgKQmJgIg8GAb775Blu3bkVZWRnS0tIQHh4OALh06RKuX78+JPO2tLTgypUrmD59OgDgxIkTUCqVeOyxx/r0nTVrFhwdHdHS0oLAwMBBz6FWqxEUFISgoCAkJSVh0qRJKCws7LcQ3rt3LzIzM1FeXg5XV1er13x9fdHQ0ICZM2faGCUR0d1hAU1EZKcqKytRUFCAkJAQ6PV6VFZW4rfffoOPjw8AwMvLC4cOHYKfnx86Ozuxfft2aLXaIZlbo9HAaDTi/fffR2dnJ+Li4hAVFdXn9g3g1tXkbdu2YcuWLTCbzXjqqafQ0dGBsrIy6HQ6GI3GPudkZ2fj4sWLWLx4MVxcXJCbmwuz2dxvgX78+HG88cYbSE1NxZQpU3D16lUAgFarxcSJE5GYmIgVK1ZgxowZiIyMhFKpxNmzZ3H+/Hm8++67Q7IfRET/xadwEBHZKZ1Oh59++gnh4eHw9vZGQkICPvjgAzzzzDMAgAMHDqC9vR2+vr54/vnnERcXB71ePyRzz5w5ExEREQgPD0dISAjmzJmDtLS0Afvv2rULb7/9NlJSUuDj44OwsDDk5OTAw8Oj3/6TJk3CkSNHsHTpUvj4+GD//v3IyMjAE0880advaWkpent7sWnTJri5uVmO+Ph4AEBoaCiys7ORn5+PJ598EgsXLsSHH34Ig8EwJHtBRHQ7hcj/PQ+JiIgIt54D/e2336Kmpma0l0JEZJd4BZqIiIiIyAYsoImIiIiIbMBbOIiIiIiIbMAr0ERERERENmABTURERERkAxbQREREREQ2YAFNRERERGQDFtBERERERDZgAU1EREREZAMW0ERERERENmABTURERERkg/8BqfPp3HIgbAIAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import sys\n", + "sys.path.insert(0, '..')\n", + "\n", + "from sklearn import datasets\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "##\n", + "# Load California Housing dataset;\n", + "##\n", + "\n", + "X, y = datasets.fetch_california_housing(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n", + "\n", + "##\n", + "# Use Ridge regression;\n", + "##\n", + "\n", + "from sklearn.linear_model import RidgeCV\n", + "\n", + "model = RidgeCV(scoring='neg_mean_absolute_error',\n", + " alphas=(0.1, 1, 10)) # default alpha values;\n", + "\n", + "from adaptivesplit.sklearn_interface.split import AdaptiveSplit\n", + "\n", + "\n", + "adsplit = AdaptiveSplit(total_sample_size=len(y_train), plotting=True)\n", + "res, fig = adsplit(X_train, y_train, model, fast_mode=True, predict=False, random_state=42)\n", + "stop = res.estimated_stop\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b624c5e52506fe96", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-02T12:29:55.574156169Z", + "start_time": "2023-10-02T12:29:32.433961985Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "# The warning is generated here: pygam.py, line 1149 - 1154\n", + "\n", + "# r2 = OrderedDict()\n", + "# r2['explained_deviance'] = 1.0 - full_d.sum() / null_d.sum()\n", + "# r2['McFadden'] = full_ll / null_ll\n", + "# r2['McFadden_adj'] = 1.0 - (full_ll - self.statistics_['edof']) / null_ll\n", + "\n", + "# Content of the \"r2\" variable:\n", + "#OrderedDict([('explained_deviance', -inf), ('McFadden', 1.7720463545418432), ('McFadden_adj', -0.8050272941666692)])\n", + "\n", + "# The explained deviance is -inf, while the McFadden pseudo-R2 is calculated properly. " + ] + }, + { + "cell_type": "markdown", + "id": "ea981223ba486e5c", + "metadata": { + "collapsed": false + }, + "source": [ + "As described, with 6 hyperparameter combinations we get the warning (specifically for two hyperparameter combinations). Let's test with the debugger what happens with 4 hyperparameter combination: n_splines=np.arange(1, 4):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5daa078", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# LinearGAM().gridsearch(gam_X, gam_y, n_splines=np.arange(1, 6))\n", + "\"\"\"\n", + "/home/giuseppe/anaconda3/envs/pytorch/lib/python3.11/site-packages/pygam/pygam.py:1150: RuntimeWarning: divide by zero encountered in scalar divide\n", + "r2['explained_deviance'] = 1.0 - full_d.sum() / null_d.sum()\n", + "\n", + "/home/giuseppe/anaconda3/envs/pytorch/lib/python3.11/site-packages/pygam/pygam.py:1150: RuntimeWarning: divide by zero encountered in scalar divide\n", + "r2['explained_deviance'] = 1.0 - full_d.sum() / null_d.sum()\n", + "\n", + "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n", + "max_iter=100, scale=None, terms=s(0) + intercept, tol=0.0001, \n", + "verbose=False)\n", + "\"\"\"\n", + "\n", + "\n", + "# LinearGAM().gridsearch(gam_X, gam_y, n_splines=np.arange(1, 4))\n", + "\"\"\"\n", + "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n", + "max_iter=100, scale=None, terms=s(0) + intercept, tol=0.0001, \n", + "verbose=False)\n", + "\"\"\"\n", + "\n", + "# By reducing the number of combination the warning disappears. Regardless, the final hyperparameter for the model are the same:\n", + "# {'scale': None, 'max_iter': 100, 'tol': 0.0001, 'callbacks': [Deviance(), Diffs()], 'verbose': False, 'terms': s(0) + intercept, 'fit_intercept': True}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pytorch", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}